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The Process Of Predictive Analytics Explained
Data Science is a multidisciplinary technology which includes statistical models, analytics techniques, visualization & several other aspects. There are so many aspects involved in the analytics process in Data Science that at times it can be very confusing. Predictive analytics, one of the major applications of Data Science is now the most discussed topic across several industries.
What Exactly Is Predictive Analytics?
Predictive Analytics can be interpreted as the process of making accurate predictions about the future by analyzing the present & historical data. This approach makes use of Big Data, Statistical Algorithms, & advanced Machine Learning techniques.
The major intent of Predictive Analytics is making use of historical and current data to come up with accurate predictions about the future. The main reason why enterprises mostly fail to achieve success with predictive analytics as they mostly ignore the prominence of historical & current data in this analytics process. Get a clear idea of the Predictive Analytics process in Data Science by joining for the real-time Data Science Training In Hyderabad program by Kelly Technologies.
Now, let’s take a look at the different steps involved in the Predictive Analytics process.
- Defining The Project
The first step involves building a clear idea about why we are performing this predictive analytics process & what we intend to predict? We should be defining the accuracy in the results that will help us to consider the project as a success. We should get to know what type of data is needed to achieve the desired results.
- Collecting The Data
In this process data is collected from the reliable sources & its credibility is verified. We can make use of automation tools that will help in the process of Data collection, cleaning, and preparation for modeling.
- Performing The Analysis
Once the data is made ready for analysis, we will be then subjecting the data to statistical methods for analysis. This process involves continuous testing & we can also make use of standard statistical techniques for to check the accuracy in the results.
In this step, we will be using statistical and machine learning approaches to build an accurate predictive model. The model is continuously trained with the data & the obtained results are tested. By deploying a feedback system we can revert the process if the obtained results aren’t accurate. If everything looks good, then we will be deploying the model in real-time & the obtained results are continuously monitored.
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