Discover illustrated tips for a successful investor pitch

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Or you consider that you need a more robust 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 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 Communicate results The final step is to evaluate paraguay phone number database 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. . 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 steps described above. Step In the first step, data on the patients' medical history must be collected. We can start from the following sample data: definition data science example sample data Here are the attributes used: npreg – Number of pregnancies glucose – The concentration of glucose in plasma bp – Blood pressure skin – Skinfold thickness bmp – Body Mass Index ped – Diabetic history age – Age income – Income Step Now that we have the data, we need to clean it and prepare it for exploratory analysis.

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