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Model Planning (Determine the model)

Posted: Sat Jan 04, 2025 7:18 am
by mdsojolh43
In this phase, you need to determine the methods and techniques to apply to establish relationships between variables. These relationships will serve as the basis for the algorithms that you will implement in the next step. To do this, you need to apply what is called exploratory data analysis (EDA), using statistical formulas and visualization tools. There are a number of tools that can be used to do model planning.

definition data science example tools modeling

R provides everything you need to do modeling and provides an ideal environment for building interpretive models.
SQL Analysis services, offered by Microsoft, which allows analysis in databases, with data mining functions and basic predictive models.
SAS/ACCESS, which can be used to access data from Hadoop and is used to create reusable flowchart models.
After exploring your data, gaining insights, and determining which algorithms to use, the next step is to apply the algorithm and build the model.

Step #4 Model Building
In this step, you will need to develop your datasets. There are two possibilities. Either you consider that the tools you currently use are sufficient to run the models. Or you consider that you need a more robust paraguay whatsapp list environment. You will need to analyze several learning techniques to build the model: classification, association, and clustering techniques. Here is a series of tools to build your model:

model building tools

Step #5 Operationalize
The implementation phase consists of creating reports, briefs, technical and programming documents. In some cases, a pilot project can also be implemented in a real-time production environment. This allows you to get a good idea of ​​the model's performance before it is deployed.

Step #6 Communicate results
The final step is to evaluate the performance of the model, check whether the initial objectives have been achieved or not, draw the main conclusions, communicate them to the stakeholders and ultimately determine whether the project is conclusive or not.

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Practical case: prevention of diabetes
To give more substance to our point, there is nothing like taking a very concrete example of a project. In this case study, we will present a project whose objective is to predict the onset of diabetes in order to improve prevention. We will go over the 6 steps described above.