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A Beginner's Guide to Predictive Analytics

In this article, we discuss what predictive analytics is, explore some examples of how it is used, and look at how it works.
Dec 2022  · 10 min read

Predictive analytics is the engine an organization needs to improve decision-making, regardless of the function or industry the organization is in. In addition, implementing predictive analytics leads to a competitive advantage that is difficult to find elsewhere.

In this article, we discuss what predictive analytics is, explore some examples of how it is used, and look at how it works.

What is Predictive Analytics?

Predictive analytics is an umbrella term that describes various statistical and data analytics techniques - including data mining, predictive modeling, and machine learning. The primary purpose of predictive analytics is to make predictions about outcomes, trends, or events based on patterns and insights from historical data.

Predictive analytics is the second of four stages of analytical capability in an organization. The four stages of analytics, in order, are:

  1. Descriptive analytics - identifying what happened in the past
  2. Diagnostic analytics - understanding why it happened
  3. Predictive analytics - predicting what will happen next
  4. Prescriptive analytics - optimizing and experimenting with how to best make it happen

Organizations must reach these analytics stages in this order since you can only effectively predict the future by understanding the past. In this way, organizations go from understanding what happened and why it happened to predicting what will happen next. An additional final stage of analytics involves fully optimized, autonomous analytical systems that continuously learn over time and are, effectively, 'intelligent.'

Predictive modeling involves two types of machine learning algorithms: supervised and unsupervised. Supervised machine learning algorithms are used to predict a target outcome and are the primary tools for predictive analytics. 

For a step-by-step walkthrough on using machine learning in predictive analytics, check out our Lyric Analysis tutorial.

There are two main types of supervised machine learning algorithms:

  • Classification models - used to predict whether observations would fall into a particular category or class. For example, predicting whether a customer will churn or not. Common classification techniques include decision trees and logistic regression models.
  • Regression models - used to predict a value. For example, predicting the click-through rate of an advert. Common methods for predicting values like this are linear regression and polynomial regression models.

On the other hand, unsupervised machine learning algorithms do not make predictions but rather seek to identify patterns in data that can then be used to label or group together similar data points. For example, one of the most popular unsupervised algorithms is k-means clustering, where similar data points, like customers, are grouped together into clusters.

Predictive analytics can also involve other statistical and data mining techniques for identifying current trends, forecasting the future, and predicting outcomes. We will discuss a few specific examples of how organizations can use predictive analytics later in this article.

Prescriptive vs Predictive Analytics

Prescriptive analytics is an organization's third stage of analytical capability that builds upon the predictive models created in the preceding stage.

While predictive analytics tells us why something is happening and what might happen next, the focus in prescriptive analytics is to optimize and experiment with the models we have already built. It answers your "what if" questions and allows you to proceed with the best possible scenario based on the information you obtain from running experiments and simulations.

Jeff Bezos, CEO of Amazon, has famously said, "Our success at Amazon is a function of how many experiments we do per year, per month, per week, per day."

Performing experiments on all aspects of analytical processes and projects is a key requirement for successful prescriptive analytics.

Predictive Analytics Examples

Predictive analytics converts data points into valuable insights that can drive and inform multiple facets of an organization. 

Here are just a few examples of how organizations can leverage predictive analytics:

Forecasting financial KPIs 

Forecasting key financial metrics like revenue, expenses, and inventory results in more effective and informed decision-making based on facts and data rather than just intuition.

Detecting and reducing fraud in banking

One of the most costly and damaging situations for a bank is fraudulent activities. Predictive analytics can help identify abnormalities and vulnerabilities that could indicate fraud so these institutions can take action swiftly.

Predicting whether a customer will default on a loan 

Offering loans is inherently risky for insurance and financial institutions. Using predictive models to predict whether a customer is likely to default on a loan is the best way for these institutions to reduce this risk significantly.

Predicting employee attrition

Predictive analytics can help improve your organization's human resource management by predicting employee attrition. This involves anticipating future hiring requirements and finding the right time to incentivize employees.

Understanding customer buying behavior

Organizations can Increase sales and conversion rates by discovering patterns behind customer purchases and exploring the reasons behind their buying behaviors. You can use customer analytics and A/B testing in Python to understand these behaviors. 

Targeting marketing campaigns to the right customers

Businesses can increase click-through rates of advertising and conversions of marketing campaigns in general by targeting the right customers at the right time. For a deeper look into the application of predictive analytics to marketing, check out DataCamp's course on predicting customer churn in Python.

Reducing manufacturing waste

Predictive analytics can help your organization understand the factors involved in manufacturing waste so that they can take action in the right areas. Using predictive models to understand these factors can result in substantial cost savings in organizations.

How Does Predictive Analytics Work?

There are many different predictive analytics tools available. The tool you choose depends on the purpose of your analysis - from business intelligence and visualization tools like Tableau and Power BI to complex programming languages like R Programming and Python.

To get an introduction to predictive analytics in Python, see our Introduction to Predictive Analytics in Python course. If you're already familiar with Python or predictive analytics, you can check out the Intermediate Predictive Analytics in Python course.

Most predictive analytics projects follow similar workflows and processes. In this section, we go over some of the main steps most commonly encountered in a project.


Every predictive analytics project should start with understanding the goal, identifying the problem, and choosing the best solution.

Predictive analytics projects aim to assist the organization in achieving its strategic objectives. When the project's goal ties back to a critical objective for the organization, it is more likely to have buy-in from every level of the organization to which it is relevant. This ensures the project is not only valuable but also successful.

Once the project's goal has been determined, this naturally leads toward a clear problem that the project needs to solve and therefore informs the solution required to get there.


Data can come from various sources, such as CSV files, databases, warehouses, and third-party applications. If not done already, these data sources should be consolidated and managed in a centralized location before you can use it in predictive analytics and modeling. This ensures that the data is secure and that quality and governance are top priorities.

As the old data analytics adage goes: "garbage in, garbage out." Try to avoid storing all your data in spreadsheets. While flexible, spreadsheets are easily edited and shared without control over the quality and security of the data within.

Additionally, consider the volume of data you have or wish to generate in the future. Building predictive models can become needlessly time-consuming and ineffective for the problems they need to solve without proper storage systems and processes to handle larger volumes of data.


The transformation step in a predictive analytics project involves cleaning, exploring, and preparing the data for the analysis or model that is to come.

When cleaning the data, you should look for missing data and outliers or suspicious values that do not make sense in the context of the problem. This goes hand-in-hand with data exploration as it is by exploring the data that you get to understand it, and anomalies become more noticeable to you.

Finally, the data must be prepared for the project's next step. The exact process here depends on the algorithm and type of analysis that needs to be done. 

For example, suppose you are fitting a linear regression model to predict click-through rates in an advertising campaign. In that case, you must consider the assumptions of linear regression and whether the data satisfies them. It is also necessary to convert any categorical variables in your data into dummy variables, known as one-hot encoding.

Before moving on to building a predictive model, be sure to split your data into training, testing, and validation sets. You fit the model to the training set, which is how it will learn the patterns in the data to make predictions. However, you must also have a validation set to iterate and improve on the model and then obtain a final, unbiased estimate of the model's accuracy using the test set.


If you are building a predictive model using a relatively simple supervised machine learning algorithm like logistic regression, then this step would require you to fit the model and evaluate the results. However, some complex algorithms, like neural networks, require careful fine-tuning and adjustments to produce accurate predictions.

An important note to remember is that many predictive models require large volumes of data to generalize accurately to the real world. If you still need to get the volumes of data that are required for these models, then you can consider other techniques that allow you to forecast or predict outcomes on a smaller scale. This also depends on your goal and the business problem you are trying to solve and involves techniques like data mining and applying various statistical methods.


The final step in a predictive analytics project is deployment. This is the project's final output or result and will serve as the medium for which the model adds value to your organization. Depending on the project and the solution you have chosen to solve your particular problem, this step can include anything from a simple report or dashboard to complex deployments into existing platforms.

Consider whether your organization has sufficient talent for more complex deployments to ensure a smooth and efficient process. For example, investing in at least 1 data engineer can go a long way toward successful model deployments.


Analytics, and predictive analytics, in particular, is not just reserved for a few tech giants and large corporations or even for just a select few within the organization. Today, organizations of all sizes use analytics, and they can be applied in nearly every industry. Additionally, predictive analytics is now a function that is distributed and owned in teams throughout an organization.

Predictive analytics can offer an incredible competitive advantage to almost every organization. However, it is crucial to consider and understand the elements that go into a successful predictive analytics project. This article provided a guide to predictive analytics, how it can be applied to business problems, and the process behind how it works.


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