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Introduction To Predictive Analytics In Data Science
Predictive Analytics in Data Science has a lot of prominence than you could possibly expect. You might be surprised to know that most of the top fortune 500 companies make use of Predictive Analytics for taking accurate business decisions. As per the view of estimates, by the end of 2022, the global market for Predictive Analytics is expected to reach $11 billion. It would surely revolutionize the world in the context of business & across several other sectors.
As the demand for Predictive Analytics is at its peaks, the demand for skilled experts in this platform has gone sky rocketing. In this blog post, let’s clearly understand what Predictive Analytics actually is.
What Exactly Is Predictive Analytics?
Predictive Analytics is an advanced concept in Data Science which involves a lot of statistical applications. Predictive Analytics accurately predicts the future by carefully understanding the patterns from the historical data & then it applies these patterns to the current data. The set of tools that are essential for the purpose of Predictive Analytics are Data Mining, Artificial Intelligence, Statistics & Machine Learning.
As every organization these days has tons of data to explore, Predictive Analytics models are the perfect fit to accurately analyze & predict the trends.
How To Perform Predictive Analytics?
The sequence of steps that are involved towards performing Predictive Analytics are
Knowing The Goals & Objectives-
The very first step in the process of Predictive Analytics is to understand the goals & objectives that are to be achieved. As a part of this process, having clear understanding of the kind of data that organization generates & also better understanding of the overview of the business is a must.
Collecting The Data-
After being very clear about the objectives & goals of the project in hand the next step is to work towards gathering the all the needed historical data. This data can be collected from different sources available with the organization. After collecting the data the next step involves cleaning the data.
Build The Predictive Model-
Building a perfect predictive model is very crucial in this process. In order to build an accurate Predictive Model using statistical analysis and data mining would be of great help. This would help in recognizing the patterns and trends in the historical data. Based on these patterns a predictive model is to be build.
In this process the predictive model is applied to the current data to predict the future outcomes.
The most important step – act on the prediction. Constant reviewing is very crucial in this regard.
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