An Extensive Guide To Understanding Predictive Models And Their Real-world Applications

Introduction 

Businesses gain a competitive advantage by predicting the future. Predictive modeling, which is a byproduct of artificial intelligence, goes one step beyond predictions. Machine Learning and business intelligence are used in predictive analytics, also known as advanced analytics. 

Data from the past is commonly used in predictive analytics models and variables. Predictive modeling projects require historical data to identify patterns and trends. Predictions are essential for businesses today because they can find better ways to serve the market, build better products, and reduce operational costs. 

Companies such as Amazon and Netflix use the predictive modeling marketing strategy to target customers more effectively and deliver a better user experience. Users are recommended products based on past purchases and browsing histories on Amazon. 

In contrast, Netflix provides TV shows and movies based on predicting analytics models. The user’s past watching habits can accurately predict the user’s preferences. 

Predictive Analytics is expected to generate more than six billion dollars in revenue by 2019. Increasing numbers of businesses are using predictive analytics techniques for everything from fraud detection to medical diagnosis by 2022, resulting in nearly 11 billion dollars in annual revenue. 

What Are Predictive Models? 

An analysis of patterns in a data set to predict future events or outcomes is known as predictive modeling, a statistical process of predicting future outcomes or events. In fact, it is an essential part of predictive analytics, a form of data analytics that focuses on forecasting trends, activity, and behavior using historical and current data. 

For example, predictive modeling can be used to determine whether a sales lead is a good one, whether spam is likely to pop up in the future or whether someone will click on or buy a link. Therefore, it is important to understand the concept of predictive modeling to improve and troubleshoot the performance of these types of business applications, which are often built into them. 

Despite the fact that predictive modeling is intended to make predictions about the future, it can also provide insights into outcomes (for example, the probability that someone will commit fraud). Since the event has already occurred (fraud has already been committed). The goal here is to make a prediction based on whether future analysis models will identify the transaction as fraudulent at a later date. In addition to forecasting future needs, predictive modeling can also be helpful in performing what-if scenarios. 

The concept of predictive modeling can be explained as a form of data mining in which historical data is analyzed to identify patterns or trends, and then that knowledge is used to estimate the future. 

Benefits of Predictive Modeling 

Following are the benefits of predictive modeling: 

• Gaining a Higher Profit Margin: Using predictive modeling, you will be able to forecast inventory, devise pricing strategies, predict customer numbers, and configure store layouts so that they are optimized for sales growth. 
• Risk Reduction: In order to detect suspicious activities, such as fraudulent transactions, cyber-attacks, or corporate spying, predictive analytics can help you detect them in advance so that you can reduce reaction times and negative consequences in the future. 
• Prioritizing Resources: With predictive modeling, sales leads can be identified and sent to inside sales teams to be converted, customer service cases can be identified, triaged, and routed appropriately, and accounts receivable workflows can be optimized based on predicting whether a customer will pay their invoice on time and whether their invoice will be escalated and triaged accordingly. 
• Optimizing Marketing Campaigns: With the assistance of predictive modeling, organizations can discover new insights about their customers and predict behaviors based on inputs. This allows them to retain valuable customers, tailor marketing strategies, and take advantage of cross-selling opportunities to promote their products and services. 

Types Of Predictive Models 

There is no need to create a predictive model from scratch for every application that requires one, which is a great advantage. Several vetted models and algorithms are used in the predictive analytics tools in order to generate a large number of useful outcomes that are applicable to a wide range of use cases. 

In recent times, there has been a great deal of progress in the area of predictive modeling. There is no doubt that the models are able to do more with the addition of more data, more powerful computing, artificial intelligence, and machine learning, as well as the overall advancement of analytics. 

Listed below are the types of predictive models: 

Clustering Model 

Based on the common attributes of the data, this model nests them together. There is a method of planning strategies that work by grouping people or things that share similar behaviors or characteristics and planning strategies at a larger scale for each group. 

For example, using historical data to determine credit risk is one of the ways that banks determine the credit risk of an applicant for a loan based on the past behavior of people in similar situations in the past. 

Classification Model 

This is the simplest model of all the models, and it categorizes the data in order to facilitate a simple and direct response to queries. For instance, one use case would be to determine whether a transaction is fraudulent. 

Time Series Model 

An evaluation of a sequence of data points over a period of time is carried out using this model. It is possible, for example, to predict how many patients will be admitted to the hospital next week, next month or the remainder of the year based on the number of stroke patients admitted to the hospital in the last four months. 

Therefore, it is more meaningful to measure and compare a single metric over time rather than simply averaging over a long period. 

Outliers Model 

By analyzing data points that are outlying, this model will be able to determine the cause. Using an outlier model for fraud detection, a bank might, for example, ask a customer if they are making a purchase outside of their normal buying habits. 

In order to illustrate the point, if, for example, an INR10, 000 card charge is made for a washer and dryer at an establishment where the cardholder has never charged any other items before but is making an INR10, 000 purchase of designer clothing at a location where the cardholder has never charged any other items before, it would not be alarming. 

Forecast Model 

It’s a well-known model that works on anything with the value of a numerical number, and it learns from historical data to perform the model. 

A restaurant, for instance, might ask the system how much corn it should order next week or how many orders it should be able to handle per day or week based on historical data that was collected in the past. 

Understanding The Types Of Predictive Modeling Algorithms 

There are two types of predictive algorithms available: those that use machine learning or those that use deep learning. It is important to note that both of these are subsets of AI. Data that is structured, such as spreadsheets or machine data, is used in machine learning (ML). A deep learning algorithm (DL) analyzes unstructured data such as text, video, social media posts, audio, images, etc. 

Listed below are some of the different types of predictive modeling algorithms: 

Random Forest 

To classify vast amounts of data, the algorithm uses a series of decision trees, which are not related in any way, and it can do so using both regression and classification techniques. 

K-Means 

The K-Means algorithm is one of the most popular and fast algorithms that groups data points based on similarities, and that’s why it’s frequently used in clustering models. As a result, it can easily create personalized offers and give them to individuals within a large group; for example, a million or more customers who share the same liking for blue leather jackets can be served quickly by this technology. 

GLM for Two Values 

Using this algorithm, the list of variables is narrowed down to find the variables that are “best fit.” The “best fit” outcome is determined by working out tipping points in the data and changing data capture to achieve the desired result, thus overcoming the disadvantages of other models, like linear regression, that have drawbacks. 

Prophet 

This algorithm is used in time-series or forecast models for capacity planning, such as estimating inventory requirements, setting sales quotas, and allocating resources to customers. As a result of its high degree of flexibility, it is capable of accommodating a wide array of useful assumptions and heuristics. 

Gradient Boosted Model 

There is also a variation of this algorithm that uses many decision trees, but unlike the Random Forest algorithm, the trees are related. One tree is built out at a time, allowing the next tree to correct any flaws in the previous tree as it was built out one at a time. Oftentimes, it is used to rank a website, such as on the results page of a search engine. 

Challenges of Predictive Modeling 

Some of the challenges of predictive modeling are: 

• Cultural and Technical Barriers: In spite of the common perception that predictive modeling is primarily a math problem, users should anticipate the organizational and technical obstacles they might face if they wish to obtain the data they need. Many data warehouses are not directly connected to systems that store user data. A data science team may not be able to share data freely with some lines of business because they feel that their data belongs to them. 
• Data Preparation: It is frequently overlooked that determining the right amount and type of data to use when creating algorithms is one of the biggest challenges in predictive modeling. Approximately 80% of a data scientist’s time is spent on this step. When data is not managed and cleaned properly, data collection is limited in its usefulness. 

In order to avoid overfitting, organizations must sort the data first. When a model is over-tested on training data, it can appear accurate, but it has less generalization ability than a model that has learned the key points. 

Conclusion 

In the field of artificial intelligence, predictive modeling has helped advance and enhance how the world operates by using data analytics. The use of various machine learning tools and techniques not only forecasts future outcomes but also determines how future decisions will impact current situations. 

Are you yet impressed by predictive models and their influence on our everyday life? If yes, you need to check out UNext’s Business Analytics program in association with IIM Indore to help you kickstart your journey in this domain.