From the course: Artificial Intelligence Foundations: Neural Networks

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Data checks and data preparation

Data checks and data preparation

From the course: Artificial Intelligence Foundations: Neural Networks

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Data checks and data preparation

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- [Instructor] In this video, we load and check the data, then perform exploratory data analysis. Here we use a Jupyter Notebook to run our code. A Jupyter Notebook lets you run Python, an open source programming language. You'll find out more in the challenge instructions where you'll only need to focus on building the neural network, which is writing six lines of code to train your network. So let's assume you've already imported the necessary libraries and modules in Python. The first step is to load the data. Here, the dataset is being pulled from the course's GitHub repo. Once the data is pulled in, you can check the first 10 lines. Note that the last column has the target that we would like to predict, which is sales. Shown here is the .info method, which gives you information on your dataset. The information contains the number of columns, column labels, column data types, memory usage, range index, and the…

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