Updated June 19, 2023
Differences Between Predictive Modeling vs Predictive Analytics
Predictive modeling employs regression models and statistical techniques to predict the probability of an outcome for various unknown events. It is a valuable tool in fields such as machine learning and artificial intelligence (AI). By applying predictive modeling, you can estimate the likelihood of an outcome based on a given set of input data. There are 2 classes of predictive models: the Parametric and Non-Parametric models. Predictive Analytics is extracting information from data to predict trends and behavior patterns. Predictive analytics uses present or past data (historical data) to predict future outcomes to drive better decisions. Predictive analytics got much more attention due to the emergence of Big Data and machine learning technologies.
Head to Head Comparison Predictive Modeling vs Predictive Analytics
Below is the top 6 Comparison between Predictive Modeling and Predictive Analytics:
Detailed Overview of Predictive Analytics and Predictive Modeling
Let’s take a look at a detailed description of Predictive Analytics and Predictive Modeling:
Predictive Analytics
Predictive analytics is used to predict the outcome of unknown future events using techniques from data mining, Statistics, Data modeling, AI to analyze and current data and predict future problems. It brings together management, information, and modeling business used to identify risks and opportunities shortly.
Predictive analytics on big data allows users to uncover patterns and relationships in structured and unstructured data and enables the organization to become proactive.
Analytical predictive analytics techniques are mainly regression and machine learning techniques.
Predictive Analytics Process
- Define Project: Define the project outcomes, deliverables, scope of the effort, and business objectives, and identify the data sets that will be used.
- Data Collection: To provide a complete view of customer interactions, data is taken from multiple sources, and by using Data mining for predictive analytics, data is prepared for analysis.
- Data Analysis: It is the process of transforming, inspecting, cleaning, and modeling data to extract useful information, arriving at a conclusion
- Statistics: Statistical Analysis enables to validation of assumptions and hypotheses and tests those using standard statistical models.
- Modeling: Predictive modeling follows an iterative process, due to which it automatically creates accurate predictive models about the future. By using multi-modal evolution, it provides several options to choose the best.
- Deployment: Predictive model deployment provides the option to deploy the analytical results into the everyday decision-making process to get results, reports, and output by automating the decisions based on the modeling.
- Model Monitoring: Models are managed and monitored to review the model performance to ensure it provides the expected results.
Application of Predictive Analytics
It can be used in many applications. Below are two examples of predictive analytics:
1. Collection Analytics:
Predictive analytics help by optimizing the allocation of resources by identifying below issues/facts:
- Effective collection agencies
- Contact strategies
- Legal actions increase recovery.
- We are reducing collection costs.
2. Customer Relationship Management (CRM):
Predictive analysis is applied to customer data to achieve CRM objectives like sales, customer service, and marketing campaigns. Organizations must analyze the products in demand or potential for high demand and identify issues that cause losing customers. Analytical CRM is applied to the entire customer lifecycle.
Predictive Modeling
You can apply predictive modeling and predictive analytics to any unknown event, whether it has occurred in the past or is expected to happen in the future. Predictive modeling solutions are in the form of data mining technology. As this is, an iterative process same algorithm is applied to data again and again iteratively so that model can learn.
Predictive Modeling Process
The predictive modeling process involves running an algorithm on data for prediction. As the process is iterative, it trains the model, which gives the fittest knowledge for business fulfillment. Below are some of the stages of analytical modeling.
1. Data Gathering and Cleansing
Gather data from all the sources to extract needful information by cleansing operations to remove noisy data so that prediction can be accurate.
2. Data Analysis/Transformation
For normalization, data need to be transformed for efficient processing. They were scaling the values to a range normalization so that the significance of data is not lost. Also, remove irrelevant elements by correlation analysis to determine the outcome.
3. Building a Predictive Model
The predictive model uses the regression technique to build the predictive model by using a classification algorithm. Identify test data and apply classification rules to check the efficiency of the classification model against test data.
4. Inferences/Evaluation
To make inferences perform cluster analysis and create data groups.
Features in Predictive Modeling:
- Data Analysis and Manipulation
Extract valuable data by using data analysis tools. Also, we can modify data, create new data, merge, or apply a filter on the data to predict the outcomes.
- Visualization :
There are tools available to generate reports in the form of interactive graphics.
- Statistics:
To confirm the prediction by using statistics tools, the relationship between variables in the data can be shown.
Predictive Modeling vs Predictive Analytics Comparison Table
Below is the Comparison table between Predictive Modeling vs Predictive Analytics.
| Predictive Modeling | Predictive Analytics |
| The business process includes :
Data Collection, Transformation, Building a model, and Evaluating/Inference the model to predict the outcome |
Business Process includes:
Define Project, Data Collection, Statistics, Modeling, Deployment, and Model monitoring. |
| Iterative Process and Runs 1 or more algorithms on data sets | Process of analyzing Historical and transactional data by statistics and data mining to predict an outcome |
| There are basically 2 classes of predictive models:
1. Parametric Model 2. Non Parametric Model |
Types of Predictive Analytics:
|
| A model is reusable (Regression Model) | Use Techniques from Data mining, modeling, Machine Learning, and Artificial Intelligence. |
| Applications: It is used in Archaeology, Auto Insurance, Health Care, etc. | Applications: It is used in Project risk management,
Fraud detection, Collection analytics, etc. |
| Types of Model Category:
Predictive Model, Descriptive Model, and Decision Model. |
Types of Analytics:
Regression technique, Machine learning technique |
Conclusion
In summary, both predictive modeling and predictive analytics revolve around the concept of utilizing data to generate valuable insights. Data, whether generated in real-time or derived from historical records, is leveraged to enhance business outcomes and achieve accurate predictions. The task of analytics or modeling is to extract the needful data from unstructured or structured data.
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