Model monitoring

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When we talk about statistics in predictive analysis, we take into consideration two well-known techniques in the area: Descriptive Statistics and Inferential Statistics.

Descriptive Statistics aims to summarize and describe a large set of data. From there, it is possible to create measures of central tendency and measures of variability or dispersion.

Inferential Statistics is the study of a sample group to draw conclusions about a larger group. Population surveys are a great example of this technique.

Statistics are essential to have a predictive analysis running correctly.

Modeling
When we bring together all the information acquired so far from predictive modeling techniques 99 acres data , we create the model.

Predictive modeling is the moment when the first ideas about possible future events begin to emerge.

Mathematical and statistical techniques are combined with the data obtained in your company, creating a model to be observed, in which the main answers you want will be easily accessible visually , updating and improving each new information generated.


After carrying out all the previous steps, it is necessary to maintain monitoring so that the processed data and mainly the information obtained from the modeling continue to be reliable .

In addition to having the answers you need more quickly and objectively to further optimize and improve your process and/or product.

Predictive Analytics Examples
Now that you know how to implement a predictive analytics strategy, let's understand where else it can be applied.

These are 5 application examples, but don't limit yourself to that and use creativity to find the answers you want.