# Simple TensorFlow Model To Predict a Linear Regression

### In this article, we will learn to create a simple TensorFlow model to predict value based on the linear regression equation.

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Join For Free**Introduction**

In this article, we will learn to create a simple TensorFlow model to predict value based on the linear regression equation.

**Steps To Build the Model**

Let us first generate the data needed for the model, although I am using less data so it won't be much accurate. Below is a screenshot of the snippet to generate data; in order to view the complete code, visit my git repo here.

Now we have generated some data based on the equation `y=mx+c`

.

Next, let us create a simple model where we provide `units`

and `input_shape`

as 1 because we are only providing *one* number in the input and expecting *one* number as the output.

Following is the snippet for creating a model:

Now let us predict something new: we have the equation `y=mx+c`

where we will have `x= 40`

, and we know while generating data, we passed `m=2`

, and `c=1`

, so as per the equation, the value should be `y =2*40+1=81`

Following is the snippet for the predicted value:

**Conclusion**

We have learned the very basic model for predicting Linear regression values.

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