Predictive analytics is a technique that uses the analysis of historical data to predict outcomes in the future. This is an optimization technique that is used in business planning to know about the possible results of an action that will be performed in the business. This way, business planners can make changes in the planning to ensure better efficiency and productivity. In general, predictive analytics can also be termed predictive modeling which is the process of data modeling and analysis used for business decision-making.
What Is Predictive Modeling?
Predictive modeling uses sets of historical data, results, and references to create a prediction of future outcomes. There are two categories of predictive models that are parametric and non-parametric models. The model that uses a specific set of parameters such as numbers is called a parametric model. A non-parametric model will use data that is not sourced from a specific set of parameters and factors. Each category of modeling has its own features and used a certain type of data to attain its objective.
Types of Predictive Models
Let’s look at the top 10 predictive modeling types that are used by different organizations for predictive analytics.
1. Decision Tree
A decision tree is a tree-like model which relates different decisions and possible outcomes. These outcomes can be the results of events, the cost of resources, or utilities. In this model, there is a tree-like structure, and each branch represents a choice between the given alternatives. The leaves on each branch are a decision choice. Based on how many categories of input variables are fed to the model, it breaks them into subsets. It is helpful in decision analysis. This model can help select the preliminary variables and can handle the missing data. The decision tree model has flexibility and adaptability allowing the users to add new possible scenarios.
2. Regression Model
Regression is a popular predictive model which estimates relationships between a dependent and one or more independent variables. This model analyses the change in the value of the dependent variable with respect to the change in the value of an independent variable. This model is used for finding the key pattern in large data sets of continuous databases. There are two types of regression models that are used for prediction or forecasting, the linear regression model, and the logistics regression model. The linear regression model is used for establishing a linear relationship between a dependent and an independent variable, but the logistics regression model is applied when there are categories of dependent variables.
3. Artificial Neural Network
In this model, a network of artificial neurons simulates the human nervous system’s capabilities of processing input signals and producing the outcomes. It is a refined model that is capable of deriving complex relations between variables. This model is vastly used as a potential tool for learning from a given dataset and making predictions on the new data. There are different algorithms used for different models of artificial neural networks. A popular algorithm is Backpropagation which is used in a supervised learning problem. Another popular algorithm is clustering where artificial neural networks are used for unsupervised learning.
4. Bayesian Statistics
This is a technique that random parameters and use the ‘degree of belief’ to predict the probability of an event occurring. Bayesian statistics is based on Bayes’ theorem which accounts for events priori and posteriori. The conditional probability tends to find the probability of one event occurring given that another event has occurred whereas Bayes’ theorem finds the probability of a prior event occurring given that the posteriori has already happened. In this model, there are conditional dependencies between random variables. It can be used for finding the causes based on the results.
5. Ensemble Learning
This model is used in the branch of machine learning with supervised learning algorithms. This model is developed by developing several different models and combining all resulting predictions made by those models. This way, it reduces the bias and variance in the model. Business planners can use this technique to identify the most appropriate model that they should use with new data.
6. Gradient Boost Model
This model is used as a machine learning technique for predictive analytics. It is also clubbed with classification and regression models. This uses predictions of weak prediction models such as decision trees. This boosting approach resamples the data sets several times and produces results as the average of the resampled data sets. Unlike most machine learning models, this model has the advantage that it is not overfitting.
7. Support Vector Machine
It is an associative kind of learning model that analyzes the data for classification and regression models. It can be defined as a discriminative classified defined hyperplane for classifying examples in a plane to separate the examples into categories with significant gaps. Then new predictions are made about the example about which side of the gap they belong to.
8. Time Series Analysis
It is a model that uses time series data that is collected at a particular interval for a certain period of time. It is a combination of traditional data mining and forecasting techniques. There are two categories of time series analysis, namely the time domain and the frequency domain. This model predicts the future of a variable at future intervals. This prediction is based on the analysis of the values at past intervals. It is popularly used in weather forecasting and stock market prediction.
9. K-nearest Neighbors (k-NN)
This model is a non-parametric technique used for regression and classification models. It is the simplest type of machine learning algorithm. In this model, the input has a k closest training examples in the feature space. For the classification model, the output is the membership of a group, and for the regression model, the output is the property of the value of an object.
10. Principle Component Analyst
It is a statistical procedure used for predictive analytics to explore the data for analysis. It is closely related to the use of the model for solving eigenvectors of a matrix. This can also be used for describing the variation in a dataset.
What Is Predictive Modelling Used For?
It is the technique used for preparing models for data mining. The historical data is used as input for these models. This data is analyzed with the help of AI and machine learning and then future outcomes are predicted based on the historical data.
How To Build Predictive Model?
There are six steps to building a predictive model as follows:
1. Collection of relevant data for target analysis: The initial step toward building a predictive model is to collect data that is relevant to your target analysis.
2. Organization of collected data into a single dataset: Organizing big data is the most complex and time-consuming task of the project. Therefore, it is important to focus on a single set of variables for the initial stages.
3. Data cleaning to avoid inaccurate model: Predictive modeling requires extensive data cleaning. This will eliminate any chances of inaccuracies in the model.
4. Create Variables to Understand the records: Create new variables to create and refine useful reports. This helps avoid any bottleneck where information is missing from the model.
5. Choose an Algorithm: Once you have your data and variables in place, you need to choose the algorithm for your model. There are plenty of algorithms and there is no one best source for everyone. It is recommended that you review the references to the algorithms used in peer-reviewed models.
6. Build the Model: Once you have all the steps performed, your next step is to build the model. For that, you can use an open-source application, licensed software, or an in-house tool.
What are the Benefits of Predictive Modeling?
One of the most prominent benefits of this technique is the capability of creating better marketing, sales, and customer service plans. In addition to that, the following are the benefits of adopting predictive modeling:
- Understanding the competition better.
- Cultivating strategies to gain a competitive edge.
- Optimization of already existing services and products.
- Better understanding of customer requirements.
- Reduced, time effort, and cost of efforts.
- Better forecasting of external factors affecting workflow and productivity.
- Financial risk identification and management.
- Better forecasting of inventory management processes.
- Identify future trends and plan according to them.
What are the Challenges of Predictive Modeling?
Although predictive modeling can be very useful for businesses in streamlining business processes and understanding market factors better, there are many major challenges that predictive modeling presents. Here are some of the challenges with this technique:
- Expertise
Predictive analytics is designed for people with deep learning of data science, statistical modeling, and languages such as Python and R. A team without the expertise required cannot leverage the predictive modeling solutions. - Application
Predictive Analytics solutions are stand-alone applications which means that if a business tries to apply that to its planning processes, it must give up the older applications. If the new team is not capable of adapting to the new solution, it can be a big problem for businesses. - Dependency on Data Accuracy
A predictive model is as accurate as the data fed to it. If a model is fed with inaccurate data, it will give inaccurate analysis which can do more harm than good. - Bias
If data is tainted with human bias and fed to the algorithm of an analytics model, it will create biased predictions. Biased prediction cannot help in the effective planning of business processes and cannot yield better What is the Future of Predictive Modeling?
In the past, the predictions were made using statistical models which were based on a sample of a large-sized database. As computer science and computation techniques advance, newer and more efficient algorithms are being introduced to make data analytics more accurate. More and more companies are recognizing the importance of data analytics in business planning and incorporating various analytics techniques such as predictive analytics. The use of artificial intelligence and machine learning models is giving promising results and bringing revolutions in the field of analytics.
Predictive Modeling FAQs
What is an example of predictive modeling?
Some examples of predictive modeling include analytical prediction in healthcare, banking, finances, logistics, and manufacturing.
What are the steps in predictive modeling?
The steps for this technique include gathering data, cleaning data, creating variables, selecting algorithms, and at last, creating the model.
What is the objective of predictive models?
The primary objective of this technique is to analyze the historical data and make predictions for future outcomes of an action taken in a business process.
How to evaluate predictive model performance?
The best way to evaluate an analytics model’s performance is by comparing the predicted values and the actual values attained.
When can predictive modeling achieve the best results?
The best way of getting the most accurate results from this technique is to feed the most accurate and unbiased data to the model and get accurate predictions.
Is predictive modeling accurate?
This technique of analysis is as accurate as the accuracy of the data fed to the model. This is why the data needs to be accumulated from the most trusted sources and curated for accuracy and unbiasedness.