Looking to learn about what predictive modelling is? AAMcourses is here to help. In this article, we will learn about predictive modeling and its process, types, and techniques.

Predictive modelling is a part of data analytics. It uses current and past data to predict future outcomes, trends, patterns, etc. It uses various statistical tools and data science techniques. Predictive modelling is used in all business aspects, such as production, marketing, finance, HR, etc.

There are several types of predictive models that will discuss later in the article. Firstly, let’s see what predictive modelling is.

What is Predictive Modelling?

Predictive modelling is a predictive technique that uses business intelligence and data mining to predict future outcomes of the business. It uses current and past data as input for the prediction. Predictive modelling helps to predict all types of business data, such as HR forecasting, sales, raw material requirement, etc.

Predictive modelling involves many types of models – ANOVA, linear regression, logistic regression, ride regression, time series, decision trees, neutral, and many more. We will discuss them in detail below.

Using the correct model for forecasting is vital, as incorrect selections can lead to inaccurate results even with correct data. It would not only waste time but also resources. Predictive modelling requires a constant update as the data keep changing every day. It is essential to update the data as per the changes to receive accurate results.

Predictive modelling helps to forecast the future based on the current data and past performances. For example, a company can use forecast its material requirement based on the order received and study the requirement of one product in the past.

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The Process of Creating a Predictive Model 

• Collection of revenant data.
• Analyse which predictive model is to be used for the data.
• Convert the selected data into an acceptable form for the selected model.
• Specify the data into sets and subsets to use in the model.
• Developing and testing the model.
• Implement the model
• Evaluate and monitor the progress.

Predictive Models 

Cluster Model

The Cluster model divides the data into groups with similar attributes. These groups with similar attributes are known as data clusters. These data clusters are analyzed by the analyst to forecast the outcomes.

Classification Model

In the classification model, every input data is classified into a specific group. There are several already established groups, and the new data is evaluated and put into a group based on its characteristics.

Outlier Model

The Outlier model works based on the unusual and abnormal data in the group or cluster. It identifies that unusual data can be a signal of fraud. Analyzing the data could help to detect fraud in the early stages.

Forecast Model

The forecast model is one of the most important predictive modelling types. The analyst performs mathematical calculations and refers to the historical data record to predict the future outcome. One of the main features of the forecast model is that if there is no numerical data, it helps to generate the numerical data. The forecast model can handle multiple performances at the same time.

Time Series Model

The 5th of the predictive modelling types is the time series model. The time series model studies past data and identifies a pattern to come to a future conclusion.

As there is a pattern in the past data, it indicates that the patterns will continue to emerge in the future. The time series model is used to predict future patterns. It helps to ensure that there is a minimum difference between the predicted and the actual outcome.

Techniques Used in Predictive Modelling

Linear Regression

Linear regression is used when two continuous data represent a linear relation. Linear regression is used to determine the value of the dependent variable based on the independent variable.

Multiple Regression

The following techniques of predictive modelling is multiple regression. Multiple regression is similar to the linear regression. The only difference here is that there are multiple independent variables available. Hence, the value of the dependent variable is calculated based on multiple variables.

Logistic Regression

In logistic regression, the dependent variable is ascertained using multiple variables. It is similar to multiple regression, but in logistic regression, the outcome is binary, i.e., such as yes or no.

Decision Tree

This technique of predictive modelling is used for data mining. A decision tree is a visualization tool. As the name hints, the decision tree is a flow chart.

The top of the tree is a question; when you answer the question, it leads you down the branches based on your response. This process continues to the point where you finally get your answer.

Random Forest

Random forest is used for classification and regression problems. In this technique, multiple trees are used. These trees are not related to each other; they have their categories.

Boosting

This method is used to learn from the results of other models, such as linear regression, multiple regression, decision trees, and so on. It studies all the models to compensate for the weakness of each model and comes up with the best possible predictive model.

Neural Networks

Neural networks are data mining tools that act as a brain to identify patterns. It is programmed for understanding data sets and forecasting the data.

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FAQs

Closing Statement

Predictive modelling is a forecasting technique based on current and past data. Businesses use it to analyze current and past data, identify patterns and trends, and use the information to forecast future outcomes.

We have discussed the 5 types of predictive models. These models differ based on the data and techniques to analyze the data. The 7 techniques of predictive modelling help to evaluate the data and provide a definitive answer to the problem and help establish the predictive models.

I hope this article about predictive modelling is informative. If you have any doubts or suggestions, create your model and post it in the comment box.