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Difference between Machine Learning and Predictive Analytics
Machine learning is a subject that belongs to Artificial Intelligence that is used to create judgments for algorithms with the help of computer science, arithmetic, and statistics. Predictive analytics deals with the historical data to make predictions for the future. In this article, we will see the differences between Machine Learning and Predictive Analytics.
Machine Learning
Machine learning is a subject that is related to AI. It has the job of making judgments for algorithms which it does with the help of arithmetic, statistics, and computer science. Machine learning is used as a tool to perform statistical analysis.
It also uses algorithms along with the computation of the resources. This computation does not take a lot of time. Many companies are using machine learning on different types of applications.
How Machine Learning Works?
Machine learning analyzes data with the help of neural networks, algorithms, and processing computers, and the results are delivered automatically. A large amount of data is needed so that machine learning can work effectively. It has the learning ability from the previous datasets.
Uses of Machine Learning
Here are a few uses of machine learning −
- Recommendation systems can be made
- Patterns in the market research can be uncovered
- The experience of the audience can be personalized
- Chatbots can be automated to provide faster services to customers
- Errors in transactions can be highlighted
Predictive Analytics
Predictive analytics is used for the manipulation of historical and current data which is used later to predict results for the future. Values for a particular variable are calculated which depends on the factor in the future. Predictive analytics can use machine learning for making predictions. Its main goal is to use historical data which can be used to make predictions for the future.
How does Predictive Analytics Work?
The techniques used in predictive analytics are −
- Descriptive analytics
- Advanced statistical models
- Mathematics
- AI algorithms
- High-volume data mining
Machine learning is needed to analyze the large amount of data quickly and effectively. A machine learning algorithm is included in the predictive analytics model which helps in providing results for the future.
Uses of Predictive Analytics
Here are a few uses of predictive analytics −
- Predictive analytics can be used by factory owners which helps in predictive maintenance. This is done by monitoring the health of machines so that parts can be replaced if needed before the failure of a machine.
- Healthcare providers can use predictive analytics to predict the outbreak of any disease and prevent it before it spreads. Governments shall also be cautioned about what to do if a disease outbreak.
- Predictive models can be used by insurance providers for the analysis of risky profiles. This will help them in making plans to pay the customers.
- The sports betting industry can use predictive analytics to calculate and decide which team will win or lose.
Similarities between Machine Learning and Predictive Analytics
Machine learning has a few similarities with predictive which are listed below −
- Both are used for the analysis of the patterns in the data
- Both work effectively on the availability of a large volume of data
- Predictive modeling is the main goal
- Both of them are used in industries like manufacturing, security, finance, etc.
- Historical data is used to make predictions
Difference between Machine Learning and Predictive Analytics
Machine learning and predictive analytics have a few differences which can be found in the table below −
| Machine Learning | Predictive Analytics |
|---|---|
| Machine learning covers different fields and predictive analytics is a part of it. | Predictive analytics is a part of machine learning. |
| The main subject related to machine learning is computer science. | The main subject for predictive analytics is statistics. |
| It is the latest technology. | Predictive analytics is not in much use now. |
| A lot of coding is needed for processing data. | Comparatively less coding is used in predictive analytics to process data. |
| Machines can make decisions and process tasks without human intervention. | Human intervention is needed to process tasks. |
| Many tools and languages are available to resolve a problem like SaaS, Python, etc. | Various tools like Excel, Minitab, etc. are required to process a task. |
| Machine learning is a vast subject and a lot of things can be learned. | Predictive analytics has a limited area and it is not as vast as machine learning. |
| It creates algorithms for the automation of predictive modeling. | In order to identify patterns, math and statistical models are used. |
| Machine learning models are designed in such a way that they learn from mistakes and this causes an increase in the data. This helps in the improvement of the performance of the models. | Predictive analytics only uses historical data and no new data is evolved while processing. |
| Different machine learning models are used for resolving complex problems. | Predictive analytics uses past data to predict future results. |
| Machine learning models can learn from experiences. | No learning feature is available in predictive analytics. |
| No manual programming is needed | Manual programming is needed. |
| Smart models are available to make decisions themselves. | No smart models are available. |
| Machine learning adopts a data-driven approach. | Predictive analytics adopts the use case-driven approach. |
| A detailed description of the problem is needed to get the solution. | There is no requirement for a detailed description of the problem to get the solution. |
| Machine learning has a target audience. | Predictive analytics does not have any target audience. |
Conclusion
Predictive analytics uses past data to provide results by doing manual programming. Machine learning delivers results automatically by using a large amount of data. A data-driven approach is adopted by machine learning while predictive analytics adopt a use case-driven approach. Both of them are being used by many big organizations.
FAQs on Machine Learning Vs. Predictive Analytics
1. In machine learning and predictive analytics which one uses human intervention to deliver results?
Predictive analytics needs human intervention to deliver results.
2. What is the root of machine learning and predictive analytics?
The root of machine learning is computer science and statistics is the root of predictive analytics.
3. Which of them is vast machine learning or predictive analytics?
Machine learning is vast in comparison to predictive analytics.
4. Which of them requires a large amount of data to work effectively and efficiently?
Machine learning needs a large amount of data to work effectively and efficiently.
5. Which of them can be used to predict results only machine learning or predictive analytics?
Predictive analytics is used to deliver results only.
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