Kevin Jacobs builds a simple neural net using Pandas and sklearn:

The first thing to notice is that our values are not normalized. The number of visitors is a number and gets larger and larger. To normalize it, we simply divide it by 100, since all numbers are below 1. The same holds for the lag. Most of the lags are lower than 30. Therefore, I will divide the lag size by 30.

Notice that there are many more approaches for normalizing the data! This is just a quick normalization on the data, but feel free to use your own normalization method. My normalization process is closely related to the MinMaxScalar normalization which can be found in sklearn (scikit-learn).

With just a few lines of Python code we can create a Multi-Layer Perceptron (MLP):

Click through for the code.

Kevin Feasel

2017-12-01

Data Science, Machine Learning, Python

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