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What Exactly Is The Process Of Predictive Analysis In Data Science?

The process of Predictive Analytics is all about interpreting with large volumes of historical & existing big data to make accurate predictions based on the analysis made. The process of Predictive Analysis relies heavily on the use of Big Data, Statistical Algorithms and it also makes use of advanced algorithms from Machine Learning technology as well.

The use of Predictive Analysis in the business sector helps the enterprises to make accurate predictions about the changes in the customers interests, market trends, forecast the demand for products or services, predicts the occurrence of business risks or any form of frauds, Sales Forecasting and much more. This technique has nowadays become an integral part of the enterprises business development strategy with Data Science. Grasp clear knowledge of Predictive Analysis process in Data Science with the help of our intense hands-on application driven Data Science Training In Hyderabad program.

Sequence Of Steps Involved In The Predictive Analytics Process:

  • Defining The Project

The very first step in the process involves understanding the business model & to know why exactly this Predictive Analytics process is is being performed and what sort of predictions is expected from the process? Then the data which is to be analyzed is identified & care should be taken to see that this data is relevant & analyzing it can lead to accurate predictions.

  • Collecting The Data

The second step involves Data Collection process where relevant data is collected from reliable sources & its credibility is verified. To speed up the process of Data Collection, we can also make use of advanced automation tools. The data is then cleaned & made ready for analysis.

  • Performing The Analysis

In this step, data is subjected to statistical analysis by the application of different statistical techniques. The accuracy in the results is a also tested in the process & it is determined whether the results that are obtained from this data would be accurate or not.

  • Modeling

Advanced statistical & Machine Learning algorithms are used to build the predictive model. The model is then trained with known data sets & the results are tested. If the results are efficient then the model is deployed in real-time & predictions are made.

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