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The 6 Steps of Predictive Analytics | 7wData

The 6 Steps of Predictive Analytics | 7wData

With tec evolution, data dependence is increasing much faster. Gone are the days when business decisions were primarily based on gut feeling or intuition. Organizations are now employing data-driven approaches all over the world. One of the most widely used data applications is ‘Predictive Analytics’. Predictive analytics is widely used for solving real-time problems, be it forecasting the weather of a place or predicting the future scope of a business.

“Predictive Analytics refers to the field that applies various quantitative methods on data to make real-time predictions.”

It provides a methodroaching and solving problems using various technologies, essentially machine learning. Predictive Analytics often makes use of machine learning algorithms and techniques to build models that make predictions.

This is the initial stage in the process of predictive analysis. This is a vital stage because we first need to understand what exactly the problem is to frame the solution. When a stakeholder approaches you with a certain problem, the first step would be to know the stakeholders’ requirements, the utilities available, the deliverables and finally, know how the solution looks from the business perspective.

Sometimes the requirements of the stakeholders may not be clearly defined. It becomes our responsibility to understand precisely what is to be predicted and whether the outcome solves the defined problem. The dynamics of the solution and the outcome completely change based on the problem definition.

Converting a business problem into an analytical one is the most important part of predictive analysis. Hence explicitly define what is to be predicted and how does the outcome look like.

This is the most time-consuming stage. Sometimes, the required data may be provided by the stakeholder, from an external database or in some cases, you may have to extract the data. It is possible that the data so collected may not be sufficient for framing the solution. You may have to collect data from many sources. Think about how much access you have to the dataset that is required.

Since the entirely on the data used, it is important to gather the most relevant data that aligns with the problem requirements. Here are a few things to be kept in mind while searching for a dataset:

Once you have the dataset ready, you now may be willing to build your predictive model. But before we start, it is crucial to know the properties of your dataanding the kind of data you have, the , the target or , and the all play a role in designing a suitable model. The main aim of EDA is to understand the data. This may be achieved by answering the below few questions:

Sometimes the data collected contains a lot of redundant data. If such data is fed as input to the model, there is a high possibility that the model makes wrong predictions. Hence it is important to perform EDA on the data to ensure that all the outliers, null values and other unnecessary elements are identified and treated. Identifying the patterns in the data makes it easier to decide the model’s parameters.

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