In the previous post we’ve seen the basics of Logistic Regression & Binary classification.

Now we’re going to see an example with **python** and **TensorFlow**.

On this example we’re going to use the dataset that shows the probability of passing an exam by taking into account **2 features: hours studied vs hours slept**.

First, we’re going to import the dependencies:

1 | # Import dependencies |

1 | data = np.genfromtxt('data_classification.csv', delimiter=',') |

Now we’re building the logistic regression model with **TensorFlow**:

1 | learning_rate = 0.01 |

Our accuracy is 86% not too bad with a dataset of only 100 elements. The optimization of the cost function is as follows:

So, our linear regression example looks like follows: