So, now we’re ready to use Keras with real data. We’ll now build a neural network which analyzes the dataset of student admissions at UCLA that we’ve previously studied.
As you follow along with this lesson, you are encouraged to work in the referenced Jupyter notebooks at the end of the page. We will present a solution to you, but please try creating your own deep learning models! Much of the value in this experience will come from playing around with the code in your own way.
To open this notebook, you have two options:
Go to the next page in the classroom (recommended)
Clone the repo from Github and open the notebook StudentAdmissionsKeras.ipynb in the student_admissions_keras folder. You can either download the repository with git clone https://github.com/udacity/deep-learning.git, or download it as an archive file from this link.
This is more of a follow-along lab. We’ll show you the steps to build the network. However, at the end of the lab you’ll be given the opportunity to improve the model, and try to improve on its performance. Here are the main steps in this lab.
Studying the data
The dataset has the following columns:
Student GPA (grades)
Score on the GRE (test)
Class rank (1-4)
First, let’s start by looking at the data. For that, we’ll use the read_csv function in pandas.
import pandas as pd
data = pd.read_csv('student_data.csv')