Students Admission model using Keras and UCLA data set

To analyze the whole dataset on Keras. We’ll analyze the following dataset of student admissions at UCLA: ‘https://stats.idre.ucla.edu/stat/data/binary.csv’

The dataset has the following columns:

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('https://stats.idre.ucla.edu/stat/data/binary.csv')
print(data)

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Here we can see that the first column is the label y, which corresponds to acceptance/rejection. Namely, a label of 1 means the student got accepted, and a label of 0 means the student got rejected.

When we plot the data, we get the following graphs, which shows that unfortunately, the data is not as nicely separable as we’d hope:

alt text

So one thing we can do is make one graph for each of the 4 ranks. In that case, we get this:

alt text

Pre-processing the data Ok, there’s a bit more hope here. It seems like the better grades and test the student has, the more likely they are to be accepted. And the rank has something to do with it. So what we’ll do is, we’ll one-hot encode the rank, and our 6 input variables will be:

The last 4 inputs will be binary variables that have a value of 1 if the student has that rank, or 0 otherwise.

So, first things first, let’s notice that the test scores have a range of 800, while the grades have a range of 4. This is a huge discrepancy, and it will affect our training. Normally, the best thing to do is to normalize the scores so they are between 0 and 1. We can do this as follows:

 data["gre"] = data["gre"]/800
 data["gpa"] = data["gpa"]/4

Now, we split our data input into X, and the labels y , and one-hot encode the output, so it appears as two classes (accepted and not accepted).

 X = np.array(data)[:,1:]
 y = np_utils.to_categorical(np.array(data["admit"]))

Building the model architecture And finally, we define the model architecture. We can use different architectures, but here’s an example:

 model = Sequential()
 model.add(Dense(128, input_dim=6))
 model.add(Activation('sigmoid'))
 model.add(Dense(32))
 model.add(Activation('sigmoid'))
 model.add(Dense(2))
 model.add(Activation('sigmoid'))
 model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
 model.summary()

The error function is given by categorical_crossentropy, which is the one we’ve been using, but there are other options. There are several optimizers which you can choose from, in order to improve your training. Here we use adam, but others that are useful are rmsprop. They use a variety of techniques that we’ll outline in the following lectures.

The model summary will tell us the following:

alt text

Training the model

Now, we train the model, with 1000 epochs. Don’t worry about the batch_size, we’ll learn it soon.

model.fit(X_train, y_train, epochs=1000, batch_size=100, verbose=0)

Evaluating the model

And finally, we can evaluate our model.

 score = model.evaluate(X_train, y_train)