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All you need to know about Predictive Modeling

Bhumika Dutta
Apr 22, 2022

Machine learning has been a revolutionary field of study in the world of technology. It has provided the machines the ability to learn and act according to the human brain with the help of artificial intelligence.

There are many models in machine learning that help in doing so, one such being predictive models. Predictive models are a type of statistical technique that can be used to predict future events or behavior. Predictive analysis is another name for it. It's one of the most effective ways for a company to see its future direction and make plans accordingly. While not perfect, this method has a high rate of accuracy, which is why it is so widely used.

In this article, we will discuss predictive models and their types.

What is predictive modeling?

Predictive modeling is the process of generating, processing, and validating a model that is used to forecast future events and outcomes using familiar results. Two of the most widely used predictive modeling techniques are regression and neural networks. Time series data mining, decision trees, and Bayesian analysis are examples of other techniques.

Companies can use predictive modeling to increase the likelihood of forecasting events, customer behavior, and financial, economic, and market risks by analyzing historical events. Businesses now have access to a sea of data as a result of the rapid shift to digital products.

Companies use big data to improve the dynamics of customer-to-business relationships. Social media, internet browsing history, cell phone data, and cloud computing platforms all contribute to this massive amount of real-time data.

However, most data is unstructured and too complex for humans to process in a reasonable amount of time. Companies use predictive modeling tools–often via computer software programs–due to the sheer volume of data. The programs sift through massive amounts of historical data in order to assess and identify patterns. The model can then provide a historical record as well as an assessment of which behaviors or events are likely to repeat themselves in the future.

Types of predictive models:

The types of predictive models are given below:

Classification Model:

The classification model is the most straightforward of the various predictive analytics models. It categorizes data based on what it has learned from historical data. Classification models are best for answering yes or no questions and providing broad analysis that aids in making decisions. The classification model's versatility—and the ease with which it can be retrained with new data—allows it to be used in a variety of industries.

Outliers Model:

The outliers model focuses on data entries that are out of the ordinary in a dataset. It can spot unusual figures on its own or in combination with other numbers and categories. The outlier model is especially useful in retail and finance for predictive analytics. It can detect anomalous data in transactions or insurance claims, for example, to detect fraud. It can also search your NetOps logs for unusual data and detect signs of impending unplanned downtime.

Clustering Model:

Based on similar attributes, the clustering model divides data into separate, nested smart groups. If an eCommerce shoe company wants to run targeted marketing campaigns for their customers, they could sift through hundreds of thousands of records to come up with a strategy that is unique to each customer. They can quickly divide customers into similar groups based on common characteristics and devise strategies for each group on a larger scale using the clustering model.

Time Series Model:

The time series model is made up of a series of data points with time as the input parameter. It develops a numerical metric based on the previous year's data and uses that metric to forecast the next three to six weeks' worth of data.

The number of daily calls received in the previous three months, sales for the previous 20 quarters or the number of patients who showed up at a specific hospital in the previous six weeks are all examples of use cases for this model.

It's a powerful way to understand how a single metric evolves over time with a level of precision that goes beyond simple averages. It also considers the seasons of the year as well as any events that may have an impact on the metric.

Forecast Model:

The forecast model, one of the most widely used predictive analytics models, deals with metric value prediction, estimating a numeric value for new data based on historical data learnings.

This model can be used anywhere there is historical numerical data. Among the possibilities are: A SaaS company can forecast how many customers it will convert in a given week, and a call center can forecast how many support calls it will receive per hour, among other things. Multiple input parameters are also taken into account by the forecast model. If a restaurant owner wants to forecast how many customers she will get in the coming week, the model will take into account a variety of variables.

How do predictive analytics models work?

Predictive analytics models have advantages and disadvantages and are best used for specific applications. One of the most significant advantages of all models is that they are reusable and adaptable to common business rules. Algorithms can be used to train and reuse models. Let us walk you through the procedure.

On the data set on which the prediction will be made, the analytical models run one or more algorithms. Because the model must be trained, it is a time-consuming process.

Multiple models are sometimes used on the same data set before a model that meets business objectives is discovered. It's important to remember that predictive analytics models are iterative in nature.

It begins with pre-processing, then data mining to determine business goals, and finally data preparation.

After the data has been prepared, it is modeled, evaluated, and finally deployed. It is iterated once the process is finished.

Because data algorithms are used in data mining and statistical analysis to help determine trends and patterns in data, they play a significant role in this analysis.

There are several different types of algorithms built into the analytics model to perform specific tasks. Time-series algorithms, association algorithms, regression algorithms, clustering algorithms, decision trees, outlier detection algorithms, and neural network algorithms are examples of these algorithms. Each algorithm accomplishes a specific task. Outlier detection algorithms, for example, identify anomalies in a dataset, whereas regression algorithms forecast continuous variables using other variables in the dataset.

How to create a predictive algorithm?

These are the steps that one needs to follow while creating any predictive modeling algorithm:

Determine how the predictive analytics models will be used and what the desired business outcomes will be.
Predictive analytics necessitates a large amount of data. The next step is to look into the data that will be used in the analysis. Organizations must decide where it will be stored, in what state it will be in, and how accessible it will be.
Once data has been discovered, it must be cleaned and gathered. It's a crucial step because predictive analytics models rely on a solid foundation to function properly.
To achieve the best results, the model must be integrated into the business process.
The model must be evaluated to see if it contributes meaningfully to the overall business processes.

Predictive modeling algorithms:

Here are the algorithms that are used in predictive modeling:

Random Forest Algorithm:

Random forest is a supervised machine learning algorithm that is commonly used to solve classification and regression problems. It creates decision trees from various samples, using the majority vote for classification and the average for regression. One of the most important characteristics of the Random Forest Algorithm is that it can handle data sets with both continuous and categorical variables, as in regression and classification. For classification problems, it produces better results.

Generalized Linear Model (GLM):

In 1972, John Nelder and Robert Wedderburn developed the Generalized Linear Model (GLiM, or GLM), an advanced statistical modeling technique. It's a catch-all term for a variety of models that allow the response variable y to have an error distribution other than the normal distribution.

Linear Regression, Logistic Regression, and Poisson Regression are among the models available. Even though the underlying relationship between the response and the predictors is not linear, GLM models allow us to build a linear relationship between them. The use of a link function, which connects the response variable to a linear model, allows for this.

Unlike Linear Regression models, the response variable's error distribution does not have to be normally distributed. The response variable's errors are assumed to follow an exponential distribution family. The name Generalized Linear Models comes from the fact that we're trying to generalize a linear regression model that can be used in these situations.

K-means algorithm:

The K-means clustering algorithm calculates centroids and then repeats the process until the best centroid is discovered. The number of clusters is presumed to be known. The flat clustering algorithm is another name for it.

The letter 'K' in K-means denotes the number of clusters found from data by the method. Data points are assigned to clusters in this method in such a way that the sum of the squared distances between them and the centroid is as small as possible. It's important to remember that reduced cluster diversity leads to more identical data points within the same cluster.

Gradient Boosting Algorithm:

Gradient boosting is a method that stands out for its predictability and speed, especially when dealing with large and complex datasets. This algorithm has produced the best results in everything from Kaggle competitions to machine learning solutions for businesses.

The main idea behind this algorithm is to build models in a sequential manner, with each model attempting to reduce the errors of the previous model. We use Gradient Boosting Regressor when the target column is continuous, and Gradient Boosting Classifier when the problem is a classification problem.

Prophet Algorithm:

The prophet is a forecasting procedure written in R and Python that is quick and produces fully automated forecasts that data scientists and analysts can fine-tune by hand. For capacity planning, such as inventory needs, sales quotas, and resource allocations, this algorithm is used in time-series or forecast models.

Forecasting is a common data science task that aids incapacity planning, goal setting, and anomaly detection for businesses. It's extremely adaptable, allowing for heuristics and a variety of useful assumptions. Despite its importance, producing reliable and high-quality forecasts poses significant challenges, particularly when there are a variety of time series and analysts with experience in time series modeling are scarce.

Also Read | What is Precision, Recall & F1 Score in Statistics?

Advantages of Predictive Models:

It enables businesses to effectively implement predictive modeling processes, which entail the use of statistics and data to forecast outcomes using data models. These models can predict everything from television ratings to sports, technological advancements, and corporate earnings.

Environmental factors, competitive intelligence, regulatory changes, and market conditions can all be factored into the mathematical calculation to produce more comprehensive views at a lower cost.

Demand forecasting, headcount planning, churn analysis, external factors, competitive analysis, fleet, and IT hardware maintenance, and financial risks are examples of specific types of forecasting that can benefit businesses.

Limitations of Predictive Modeling:

Here are some limitations of this predictive modeling method:

There can be some data labeling errors. Reinforcement learning or generative adversarial networks can be used to overcome these errors (GANs).

Massive data sets are in short supply, making machine learning difficult to train. "One-shot learning," in which a machine learns from a small number of demonstrations rather than a large data set, could be a solution.

The inability of the machine to explain what it did and why it did it. Machines do not "think" or "learn" in the same way that humans do. Similarly, their computations can be extremely complex, to the point where humans have difficulty finding, let alone following, the logic. All of this makes it difficult for a machine or even humans to explain their work.

Learning generalizability, or rather its lack thereof. Machines, unlike humans, have a hard time applying what they've learned. To put it another way, they have trouble applying what they've learned to new situations. Everything it has learned is only applicable to one use case. Transfer learning may be a solution for making predictive modeling with machine learning reusable—that is, useful in more than one use case.

There's also data and algorithmic bias. Non-representation has the potential to skew results and lead to the mistreatment of large groups of people. Baked-in biases are also difficult to detect and eliminate later. Biases, in other words, have a tendency to perpetuate themselves. This is a moving target, and no definitive solution has yet been found.

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All you need to know about Predictive Modeling

What is predictive modeling?

Types of predictive models:

Classification Model:

Outliers Model:

Clustering Model:

Time Series Model:

Forecast Model:

How do predictive analytics models work?

How to create a predictive algorithm?

Predictive modeling algorithms:

Random Forest Algorithm:

Generalized Linear Model (GLM):

K-means algorithm:

Gradient Boosting Algorithm:

Prophet Algorithm:

Advantages of Predictive Models:

Limitations of Predictive Modeling:

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