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Prediction Process Involved In Predictive Analytics

Data Science has nowadays become a must to have technology in the enterprises business development process. The applications of Data Science aren’t just helping the businesses to scale & grow but are also helping the business stakeholders to make strategic decisions based on its future predictions. The process of Predictive Analytics in Data Science has now become the heart of the enterprises decision-making process. The process of setting up the   components & resources for the process of predictive analytics is similar to the process of setting up an IT data center or refitting a factory. At present, there’s a lot of demand for the skilled & certified Data Science professionals who are experts in handling the operational process of Predictive Analytics.

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Now, in this post, let’s look at the prediction process involved in Predictive Analytics.

Prediction Process Involved In Predictive Analytics:

Problem Identification

The very first step in the process is to identify the problem that needs to be answered. Example: What is the probability of a product getting sold if its price gets increased by $4?

Determining The Outcome & Predictors

Analyze the patterns from Customer data, Store and Product attributes like previous pricing and the competitor price.

Explore Data & Segregate Data

This process involves exploring the data & preparing it for the data for outliers by eliminating all the anomalies.

Test The Models

From Data Exploration we will be get a clear understanding that Customer’s age and their income levels would also impact the sales. Then by using the existing historical data we can plot a scatter plot and then based on which we can get a probability on the overall sales.

Applying The Model  

Models are tested with already known historical data to know its accuracy. For example, we can take the 2017 & 2018 data to test for a model for 2019 outcome whose results are actually known beforehand.  

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