Data Science Predicting the Future!

According to Harvard Business Review, Data Scientist is the sexiest job in the 21st century. One of the major subjects of Data Science is Predictive Analysis, also known as Data Prediction. The foundations of Data Prediction are built on Machine Learning Algorithms. Machine Learning renders a way of identifying patterns in data and using them to automate data prediction. Ready to know more? Let’s dive in.

What is Data Prediction?

Data prediction is a branch of Data Science which deals with statistical techniques to estimate or predict future outcomes. It helps to understand future occurrences by analyzing the past.

Ex: Several e-commerce websites can predict a customer’s behaviour by analyzing the previous searches and choices of that customer.

Forms of Data Analysis

There are three types of data analytics broadly:

Descriptive Analytics: Aggregating big data and pulling powerful insights from the stockpile.

Predictive Analytics: Data analysis to predict future outcomes.

Prescriptive Analytics: Recommending the best course of action for any pre-specified outcome.

Big Data Prediction using ML Algorithms

How does it work?

Data prediction and machine learning go hand in hand. Data mining is performed before statistical analysis. Machine learning algorithms can design a predictive analytics model that can be trained over time to respond to new data inputs. Businesses need data prediction models to optimize and uncover new statistical patterns.

Machine Learning Models for Data Prediction

Let’s have a look over some of the most widely used predictive models:

Regression: In statistics, regression analysis is defined as the process of finding a relationship between variables by plotting data points to find key patterns. Two types of regression models are usually employed for data prediction - Linear and Logistic Regression.

Neural Networks: Familiar with neurons in a human body? They are the basic building blocks of the human neural system. Likewise, for big data analytics, there are artificial neural networks in deep learning used to solve complex pattern recognition problems. We will discuss more them later in this section.

Decision Tree: It is a data structure consisting of internal nodes as attributes, branches as outcomes, and leaf nodes as decisions.

Neural Networks: Elementary Units for Data Prediction

From business to education, from finance to engineering, in every sector, we already know that a vast amount of data is associated with the working of an enterprise. Data science experts deal with this data to profit organizations. Neural networks are their tool for this purpose. Neural networks consist of several interconnected nodes. These nodes contain vital information from the past. Neural networks are capable of modifying themselves as they learn from new input data sets.

How can Machine Learning boost Data prediction?

To cater to your business needs, you have to develop the right environment for predictive data analysis. To do the same, Machine learning algorithms are there to help you. They can do everything, ranging from optimizing workflows to suggesting strategies for better business growth. As we have already seen, neural networks are incredibly useful for analyzing large data sets. Business analysts can resort to neural networks now to build a perfect data prediction model.

Applications of Data Prediction using Machine Learning

Banking and Finance

  • Fraud detection

  • Measuring market risk

  • Predicting policy’s future


  • Customer churn prediction

  • Fraud transaction detection

  • Identifying customers with similar attributes


  • Disease likeliness

  • Pandemic recovery

  • Drug discovery


  • Anomaly detection

  • Understanding user behavior

  • Enhancing data security

Key Takeaways

We started with defining data prediction models and now you know, how you can build one for your business using machine learning algorithms. We also threw light on the significance of neural networks in designing a data prediction model. Contact us today if your doubts are still not cleared. Follow this space for more such stuff.

Tech Stack

R, Python, MATLAB, SQL.


Microsoft Azure, Rapidminer, Hadoop, IBM SPSS, Eviews.