A model widely used in traditional statistics is the linear regression model. In this article, the objective is to follow the step-by-step implementation of this type of models using *Tensor Flow*. We are going to represent a simple linear regression.

For our study, we will analyze the age of the children on the **x** axis and the height of the children on the **y** axis.

We will try to predict the height of the children, using their age, applying simple linear regression.

The regression model will be represented in *Tensor Flow*. We will try to train the model to find the best **W** and** b**. Academic source of this example link.

We will use a spreadsheet to explore the results in a traditional way:

Now, what do we look for with this model of ML?, Recalling our linear function y = **W**x + **b**

One of the goals is to learn, to find the best **W **and **b**.

The best values for W and b will reduce the cost. As shown in the following figure.

With the lowest cost, we radically improve the prediction, as shown in the following figure:

Let’s develop the model in *Tensor Flow*. *If you need to see the implementation code, you can see them in detail in stackoverflow documentation. [Best display for code and comments]

I also included **Tensor Board** in our study to analyze the model:

And the tracing of the cost function:

If we compare our study using a traditional template and our automatic model, it is evident that we have better accuracy and also the whole scale to ingest a greater volume of data. Are you ready for more magic?

Version of the article in Spanish here.