A Complete Guide to Predictive Analytics

Austin Chia, contributor to the CareerFoundry Blog.

Data analytics plays a huge role in many companies, in creating better business strategies and making more informed decisions.

Predictive analytics is at the forefront of this trend, providing businesses with insights into what may happen in the future.

Being one of the four key types of data analytics, predictive analytics is one of the most commonly used analysis methods.

With the possibility to predict future trends, understanding this exciting area is key to conducting proper data analysis. In this complete guide, we’ll explore all aspects of predictive analytics and what this field entails.

We’ll cover:

  1. What is predictive analytics?
  2. Types of predictive analytics
  3. Predictive modeling techniques
  4. Data preparation and feature selection
  5. Real-world applications of predictive analytics
  6. Ethical and legal considerations in predictive analytics
  7. Key takeaways

Let’s begin!

1. What is predictive analytics?

Predictive analytics is the science of using data to make predictions about the future.

It’s a form of data analytics that focuses on using statistical modeling and machine learning algorithms to identify patterns and trends. These models are used to make predictions about the future.

However, do take note that predictive analytics is not to be confused with prescriptive analytics, which makes recommendations on what to do given the data.

In fact, predictive analytics is one step before prescriptive analytics, and is the foundation for more advanced analysis. For further reading, our comparison of predictive vs prescriptive analytics will shed more light on the differences between the two.

Now that you’ve got a clearer picture of what predictive analytics is, let’s look into the types of predictive analytics.

2. Types of predictive analytics

Predictive analytics can be broadly divided into three main types:

  • Clustering
  • Time series
  • Classification

Now let’s have a closer look at each of them.

1. Clustering

Clustering is the process of segmenting data into distinct groups according to similar characteristics. This allows for further analysis and understanding natural grouping of the data.

Through clustering, you’ll be able to pick out similarities when you notice data points appearing close to each other. This helps with detecting patterns that may have otherwise gone unnoticed.

2. Time series

Time series predictive analytics looks at data trends over a specific period of time. This allows for the prediction of future values and the identification of any patterns or outliers from past data.

Time series is particularly useful when you want to predict sales, stock prices, and website visitor numbers—anything that is time-sensitive and can fluctuate over time.

If you’d like to learn more, check out our guide to time series analysis.

3. Classification

Classification is the process of categorizing data into distinct classes based on certain characteristics. It helps to summarize datasets into discrete groups that make further analysis easier.

In classification predictive analytics, supervised machine learning models are typically used. These models will help with grouping and segmentation.

Each of these types uses different modeling techniques, which we’ll explore in the next section.

3. Predictive modeling techniques

Predictive models are mathematical equations and algorithms used to predict a future outcome, such as customer churn or sales performance.

There’s a wide range of predictive modeling techniques available, such as:

  • Regression
  • Decision trees
  • Neural networks (A subset of machine learning and the driving force between generative AI tools such as ChatGPT)
  • Random forests
  • K-means clustering
  • K-nearest neighbors (k-NN)
  • Autoregressive integrated moving average (ARIMA)

The technique used will depend on the available data and the results you want to obtain.

To help you understand their context, I’ve divided them up based on their type.


Regression techniques such as logistic regression belong to the classification type of predictive analytics and are used to predict probabilities.

Decision trees are also used for classification, but they focus on finding the most important relationships between variables.

Neural networks involve feeding data into an artificial network in order to detect patterns or trends that would otherwise be undetectable by human analysis.

Random forests use multiple decision trees for predictions, making them more accurate than single decision tree models. Both of them are used in classification as well.


For clustering predictions, you’ll most likely encounter k-means clustering and k-nearest neighbors (k-NN) techniques.

K-means clustering is used to find natural clusters in the data by minimizing within-cluster variability.

k-NN uses the nearest neighbors of a point to predict its class or label.

Time series

Finally, ARIMA is a time series technique used for forecasting future values based on past observations.

It involves using autoregression, which looks at past values to predict future ones, and moving average, which is used to smooth out the fluctuations in time series data.

ARIMA models are mainly used in time series predictive analytics to identify long-term trends or seasonal patterns.

I’ll now share with you more about data preparation in predictive analytics.

4. Data preparation and feature selection

Data preparation is an essential step in predictive analytics because it helps to clean and format the data so that it’s ready for analysis. This means selecting relevant attributes, removing unnecessary data points, and dealing with missing values.

Feature selection is a part of the data preparation phase, where you can determine which variables will impact the outcome most. It’s used to prevent overfitting.

Too many features will produce overfitting, so you’ll need to reduce the number of features or variables used to get accurate results.

Simply put, overfitting is when the model is too closely fitted to the training data to the point where it begins to memorize the training data instead of learning from it. Thus, the model cannot make predictions based on new data.

5. Real-world applications of predictive analytics

Here are some applications you may come across for predictive analytics:

Customer segmentation

Customer segmentation divides customers into groups based on different characteristics and predicts customer behavior. This is most commonly used in marketing, where different products target different customer demographics.

Why is this important to marketers and marketing analysts?

Having a clearer understanding of how and where their customers interact with marketing campaigns can help marketers better target them.

This allows them to develop more effective and personalized marketing strategies, resulting in increased customer engagement and conversions.

Fraud detection

Fraud detection techniques can be used to identify patterns of fraudulent behavior, such as suspicious credit card transactions or accounts with unusually high levels of activity.

These techniques use machine learning algorithms to detect anomalies that may indicate fraud by flagging them for manual review.

Fraud prevention helps to protect businesses and customers from the financial loss associated with fraudulent activities.

At-risk patient detection

The applications of predictive analytics can be used in saving lives in healthcare as well!

In a study carried out in 2021, at-risk patient detection was used to identify patients at high risk of developing COVID-19.

Using a machine-learning algorithm they developed to analyze health records, they detected subtle patterns that lead to early diagnosis.

It was capable of identifying the survival likelihood of a given patient.

This is just the tip of the iceberg when it comes to the potential applications of predictive analytics.

However, as with all new breakthrough technologies, some concerns for ethical use and data privacy arise.

When using predictive analytics, several ethical and legal considerations must be taken into account.

According to research in The Proceedings of the National Academy of Sciences (PNAS) journal, Facebook “Likes” were able to successfully predict race, IQ, and even sexuality.

This brings some serious questions about the ethical use of predictive analytics.

The primary concern is that predictive analytics can be used for discriminatory purposes, such as targeting specific demographics or unfairly determining someone’s eligibility for a job or loan.

Therefore, it is important to consider and respect the rights of individuals when collecting and analyzing data.

Here are some considerations:

  1. Transparency: Predictive analytics should be transparent and explainable, so users can understand how decisions are being made.
  2. Accuracy: Predictive analytics models must be accurate to avoid errors or bias in the predictions they make.
  3. Data privacy: All data collected should be kept secure and not used for any purpose other than the one for which it was collected.
  4. Data quality: All data used in predictive analytics should be of high quality to ensure accurate predictions.
  5. Algorithm fairness: Predictive algorithms should be fair and unbiased, avoiding any discrimination against individuals or groups.
  6. User control: Users should be in control of the data collected from them and be aware of how it is being used.
  7. Regulatory compliance: Organizations using predictive analytics should ensure they are compliant with relevant regulations and laws.

These are just some of the ethical and legal considerations to keep in mind when working with predictive analytics. As technology continues to evolve, more considerations may arise.

7. Key takeaways

In this article, we’ve learned that:

  • Predictive analytics is a type of data analytics that uses machine learning algorithms and statistical modeling to predict future outcomes.
  • Clustering, time series, and classification are types of predictive analytics.
  • It can be used for customer segmentation, fraud detection, and at-risk patient detection in healthcare.
  • Ethical and legal considerations must be kept in mind when working with predictive analytics, such as data privacy and accuracy.

In conclusion, predictive analytics can drive better business decisions and improve operations. With its wide range of applications, it’s certainly something to look out for!

To learn more about predictive analytics and the exciting wider field of data analytics, try this free 5-day data analytics short course.

For more related reading on the world of analytics, check out the following guides:

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