Amidst all the hype around Big Data, you must be hearing the term “Machine Learning”. No matter which service your business is catering to, you would love to imbibe Machine Learning to drive target-oriented solutions.
Machine Learning isn’t new, but the field of big data is revitalizing it. Today, more organizations rely on ML models to scale their operations and support staff in working better.
Machine Learning (ML) focuses on teaching computers to learn from data and to improve with experience. With fast data processing speed, ML uncovers hidden insights from data. You can even confirm and challenge underlying assumptions with ML.
Predictive Analysis focuses on making predictions. Due to near-accurate predictions, it is particularly gaining popularity in the business analysis world. It usually involves using various statistical models, techniques, and tools to develop valuable insights.
Machines learn from the data (Machine Learning) and develop suggestions/ predictions (Predictive Analysis). Such predictions help businesses solve problems and boost their sales by helping them make better decisions.
Are you interested to know more about this? I have come up with a blog. The blog will tell you about Machine Learning, Predictive Analysis and will help you differentiate the two.
Predictive Analytics vs Machine Learning
Machine Learning is a subfield of artificial intelligence (Intelligence associated with machines) defined as the ability of the machines to imitate the intelligence attribute of humans. Machines can now learn and analyze the data and come up with their suggestions. Such suggestions are known as predictions and technology called Predictive Analysis.
Are you confused about the whole process? Well! These machines are nothing but computer systems. These machines are fed with the data first, and by using specific algorithms and statistical models, they draw inferences from it. Based on these inferences, machines can now predict solutions for the problems/ scenarios they have not explicitly programmed.
Day-To-Day Routine Example
For a start, let us understand it from our day-to-day routine:
You have a friend over for dinner. He is a friend who is always late, everywhere. Usually, he is 10 minutes late (Previous experience/ data available). You cook your dinner 10 minutes late. But it is raining today. He came 15 minutes late, explaining that it was hard to drive while it was raining.
You learn and expect him to be 10 minutes late on regular days and 15 minutes late when it is raining (Machine Learning). Based on your experience/ learning, you can now predict his behaviour for the next meeting and will cook accordingly (Predictive Analysis).
Computers use Predictive Analysis in the same way. Historical data is used for Machine Learning to predict new or different information values. They determine what is most likely to happen based on the data from what has happened in the past.
Real-World example- Predictive Analytics in Manufacturing
The manufacturing industry wants to check the state of its equipment. Predictive Analysis can help the industry out! Here the data set will include temperature, running time, power level durations, and error messages generated from the machine. It can be achieved using sensors.
The data is now fed to the computer. The computer here is well accompanied by a predictive model that predicts the machine’s state based on sensor values. Based on the sensor’s observations and the machine’s condition, the predictive model will learn at what value the machine breaks. Once you have trained your predictive model, it will predict and flag the state of machines.
Hence, the Predictive Analysis will help you limit or prevent any impact of your inventory on your production pipeline.
Analyzing the data to understand different patterns is known as machine learning, and drawing inferences from their analysis is called predictive analysis.
A smart predictive model relies on the quantity and quality of data to produce reliable predictions. While the data quantity offers more reliability of forecasts, the better data quality ensures better decision-making power for complex enterprises.
Moreover, counting on real-time data to fuel the predictive analytics model results in more accurate predictions.
- Predictive Analysis is driven by data on which we apply some analysis/ analytic techniques and is used to drive future Insights. Based on what observations are telling us, we can conclude what can be possible solutions for the problem.
- We can’t only predict the future to make informed decisions regarding risks and opportunities but also can use Predictive Analysis for past events with unknown causes. Past events can be explained after discovering patterns or trends in the data.
- We apply mathematical and statistical techniques and domain knowledge of the problem being solved.
Hence, machines learn/ adapt to behave in a certain way without explicit instructions for that particular problem. It can predict solutions to complicated issues.
How does Machine Learning with Predictive Analysis Will Benefit Businesses?
Today, every organization looks up to Machine Learning and Predictive Analysis to help solve difficult problems and discover new opportunities. Common benefits include:
Improving pattern detection with multiple analytics can prevent criminal behaviour. High-performance fraud detection analytics examines all actions on a network in real-time. Hence, it ensures cybersecurity. Fraud prevention with Machine Learning indicates fraud, zero-day vulnerabilities, and advanced persistent threats.
Optimizing Marketing Campaigns:
Predictive Analytics in marketing will benefit your businesses to attract, retain and grow their most profitable customers. You can determine customer responses or purchases and promote cross-selling opportunities.
Retailers or marketing service providers use Predictive Analytics in advertising to find trends in the browsing history of website visitors and personalize advertisements for their customers.
Many businesses use predictive models to forecast inventory and manage resources. For example, hotels try to predict the number of guests for a particular season to maximize occupancy, and airlines use predictive Machine Learning to set ticket prices.
Credit scores are used to assess a buyer’s likelihood of purchases. A credit score is a number generated by Machine Learning prediction models that help find a person’s creditworthiness.
Also, predictive analysis helps banks control the loss to an acceptable level (banking risk management). Bank loan default can be predicted using predictive analysis. Banks collect and store more and more user data to revise their loan strategies accordingly.
Similarly, predictive risk analytics can exponentially reduce the risk for many other businesses.
Why Should You Connect to Ksolves?
Machine Learning and Predictive Analysis, helps businesses in intelligent decision making, maintaining business continuity, automating repetitive tasks, and more. Why not fuel your business with the same? Connect to experts in the technology to infuse customized and ready-to-go processes into your business.
Contact us at firstname.lastname@example.org, or call us on +91 8130704295 for complete machine learning consulting services.
Machine Learning is a tool that automates predictive modelling by generating training algorithms. These algorithms train on the current and past data to examine patterns and finally respond to new data or values, delivering the results the business needs.
Hence, we can conclude that Predictive Analytics and Machine Learning go hand-in-hand as predictive analytics typically includes Machine Learning algorithms.
- What kind of research questions are most suitable for Machine Learning?
Any company who wants to automate their business by getting useful insights from their data can make use of ML research.
- How does Machine Learning work?
Machine Learning, at its core, is a data deduction technique. It uses different algorithms to identify key insights in the data.
- What are the prerequisites for Predictive Analysis?
We need good quantity and quality of data. Based on the use case, we need various algorithms to build and test predictive models based on observed patterns.