This problem is perfectly suited for a neural network. Your model will have 40 input nodes (this is fine), then you will have some arbitrary hidden layers, you need to tune this, and 20 outputs. After the training process you can even get a probability for each of them. This can be used to rank suggestions for potential students!
How to do this
Load the data to memory
Depending on the means by which your data is stored this step will be different. However, the goal is to go from the raw file source to either Python Numpy array or a Pandas DataFrame. I will assume your data is structured as follows
and is saved as a .csv file.
Let's load our data into an X and Y matrix. We will be encoding the labels for 'degree' as values. Make sure these are all well spelled otherwise a new label will be created for the mispelled ones.
import pandas as pd
import numpy as np
df = pd.read_csv('test1.csv')
df['Degree'] = df['Degree'].astype('category')
df["Degree_encoding"] = df["Degree"].cat.codes
X = np.asarray(df.loc[:, df.columns != 'Degree'])
Y = df['Degree_encoding']
Applying a neural network to this dataset
First we will split the data randomly into a training and testing set. This is used to evaluate our model while maintaining that we are not overfitting. Then we identify the number of output classes. Then we reshape the input matrices such that they have a channel in their last dimension, this is how data flows through the model. Then we will categorize our outputs as one-hot encoded vectors.
from sklearn.model_selection import train_test_split
# Split the data
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, shuffle= True)
# The known number of output classes.
num_classes = len(set(df["Degree_encoding"]))
# Input dimensions
shape = X.shape[1::]
# We need to add a channels dimension to our data
# Channels go last for TensorFlow backend in Keras
x_train_reshaped = x_train.reshape((x_train.shape,) + shape)
x_test_reshaped = x_test.reshape((x_test.shape,) + shape)
input_shape = shape
# Convert class vectors to binary class matrices. This uses 1 hot encoding.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
We then design our model
model = Sequential()
You can use
model.summary() to get a description of these layers. Then we are ready to train our model!
epochs = 100
batch_size = 128