Machine Learning and Predictive Analytics

Machine Learning and Predictive Analysis is the high-octane duo needed by almost all known big organizations under the sun. This pair of tools turn complex to simple. I am going to unmask both of them and share with you their definition, role in organizations, perks, and work flow.

Machine Learning and Predictive Analysis work independently or aggregately to help a business prosper. The key to an organizer’s success is figuring out how these duo work and how to extract maximum yield from them.

So, what is Predictive Analysis? It is a branch of analytics to predict about future outcomes. Predictive Analysis models comprehend relationships among various factors to evaluate risks and mitigate them. Application of Predictive Analysis in business can effectively interpret large amount of data in limited time for maximum benefit. Techniques such as data mining, data collecting, statistics, data modeling, artificial intelligence, and machine learning are the building blocks of Predictive Analysis models to make accurate predictions of future events. Predictive Analysis brings forward a concrete interpretation of data with structured algorithms rather than mere assumptions, which helps organizations to probe into outcome anticipation. Data collection and mining, text analytics, statistics allow businessmen to design Predictive intelligence by demystifying patterns and relationships between structured and unstructured data. The Predictive Analysis process comprises 7 steps which are mentioned below:

  1. Define Project outcomes and objectives
  2. Collect data from numerous sources for analysis
  3. Evaluate, clean, transform, and model data to determine useful information
  4. Validate hypothesis with standard statistical models
  5. Create appropriate predictive models
  6. Implement analytical results in real-time scenario to get results
  7. Manage and monitor models to ensure effective results

I hope by now you have got a clear picture of what Predictive Analysis is. Let me make it clearer for you by citing an example. The navigation system in your google map is apt example of deployment of Predictive Analysis. You feed in specific instructions into your system, they determine the easiest way to reach your destination in a particular time.

Having said all these, there is still a certain confusion about the relationship between this dynamic duo: Machine Learning and Predictive Analysis. Machine Learning is the ability of computers to learn automatically without being explicitly programmed. Machine learning algorithms are categorized into supervised and unsupervised versions. Supervised machine learning algorithms can be used for predictive analytics. In supervised machine learning, the algorithm is trained with data to predict future data. We feed in data and use this fed data to analyze future outcome of another data set. But not all predictive analytics needs to be done using machine learning approaches.

The main difference between the two techniques are the assumptions of the data generation process. In the statistical modelling approach, the assumption is that data is generated from a consistent but random data generation process. Hence the term 'modelling the data', and assuming various distributions etc. In machine learning, the is no such assumption. Predictive analytics is finding pattern in data and predict future outcomes by using that pattern.

It can be said that "All Machine Learning is Predictive Analysis but all Predictive Analysis is not Machine Learning”.