2. Neural Networks / L7. Keras - Lab : Student Admissions in Keras

Predicting Student Admissions with Neural Networks in Keras

 · Loading the data

# Importing pandas and numpy
import pandas as pd
import numpy as np
 
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
 
# Printing out the first 10 rows of our data
data[:10]

>>

 · Plotting the data

# Importing matplotlib
import matplotlib.pyplot as plt
# Function to help us plot
 
def plot_points(data):
    X = np.array(data[["gre","gpa"]])
    y = np.array(data["admit"])
    admitted = X[np.argwhere(y==1)]
    rejected = X[np.argwhere(y==0)]
    plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'red', edgecolor = 'k')
    plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'cyan', edgecolor = 'k')
    plt.xlabel('Test (GRE)')
    plt.ylabel('Grades (GPA)')
    
# Plotting the points
plot_points(data)
plt.show()

>>

 · Make 4 plots, each one for each rank

# Make 4 plots, each one for each rank.
# Separating the ranks
data_rank1 = data[data["rank"]==1]
data_rank2 = data[data["rank"]==2]
data_rank3 = data[data["rank"]==3]
data_rank4 = data[data["rank"]==4]
# Plotting the graphs
plot_points(data_rank1)
plt.title("Rank 1")
plt.show()
plot_points(data_rank2)
plt.title("Rank 2")
plt.show()
plot_points(data_rank3)
plt.title("Rank 3")
plt.show()
plot_points(data_rank4)
plt.title("Rank 4")
plt.show()

>>

 · One-hot encoding the rank

# One-hot encoding the rank
# Make dummy variables for rank
one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)
# Drop the previous rank column
one_hot_data = one_hot_data.drop('rank', axis=1)
# Print the first 10 rows of our data
one_hot_data[:10]

>>

 · Scaling the data

1

2

3

4

5

6

7

# Scaling the data

# Copying our data

processed_data = one_hot_data[:]

# Scaling the columns

processed_data['gre'] = processed_data['gre']/800

processed_data['gpa'] = processed_data['gpa']/4.0

processed_data[:10]

cs

>>

 · Splitting the data into Training and Testing

# Splitting the data into Training and Testing
sample = np.random.choice(processed_data.index, size=int(len(processed_data)*0.9), replace=False)
train_data, test_data = processed_data.iloc[sample], processed_data.drop(sample)
print("Number of training samples is", len(train_data))
print("Number of testing samples is", len(test_data))
print(train_data[:10])
print(test_data[:10])

>>

 · Splitting the data into features and targets (labels)

import keras
 
# Separate data and one-hot encode the output
# Note: We're also turning the data into numpy arrays, in order to train the model in Keras
features = np.array(train_data.drop('admit', axis=1))
targets = np.array(keras.utils.to_categorical(train_data['admit'], 2))
features_test = np.array(test_data.drop('admit', axis=1))
targets_test = np.array(keras.utils.to_categorical(test_data['admit'], 2))
 
print(features[:10])
print(targets[:10])

>>

 · Defining the model architecture

# Imports
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
 
# Building the model
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(6,)))
model.add(Dropout(.2))
model.add(Dense(64, activation='relu'))
model.add(Dropout(.1))
model.add(Dense(2, activation='softmax'))
 
# Compiling the model
model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

>>

 · Training the model

# Training the model
model.fit(features, targets, epochs=200, batch_size=100, verbose=0)

 · Scoring the model

# Evaluating the model on the training and testing set
score = model.evaluate(features, targets)
print("\n Training Accuracy:", score[1])
score = model.evaluate(features_test, targets_test)
print("\n Testing Accuracy:", score[1])

>>

 · 전체코드

student_data.csv

StudentAdmissionsKeras.ipynb

# Loading the data
# Importing pandas and numpy
import pandas as pd
import numpy as np
 
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
 
# Printing out the first 10 rows of our data
data[:10]
 
# Plotting the data
# Importing matplotlib
import matplotlib.pyplot as plt
 
# Function to help us plot
def plot_points(data):
    X = np.array(data[["gre","gpa"]])
    y = np.array(data["admit"])
    admitted = X[np.argwhere(y==1)]
    rejected = X[np.argwhere(y==0)]
    plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'red', edgecolor = 'k')
    plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'cyan', edgecolor = 'k')
    plt.xlabel('Test (GRE)')
    plt.ylabel('Grades (GPA)')
    
# Plotting the points
plot_points(data)
plt.show()
 
# Make 4 plots, each one for each rank.
# Separating the ranks
data_rank1 = data[data["rank"]==1]
data_rank2 = data[data["rank"]==2]
data_rank3 = data[data["rank"]==3]
data_rank4 = data[data["rank"]==4]
 
# Plotting the graphs
plot_points(data_rank1)
plt.title("Rank 1")
plt.show()
plot_points(data_rank2)
plt.title("Rank 2")
plt.show()
plot_points(data_rank3)
plt.title("Rank 3")
plt.show()
plot_points(data_rank4)
plt.title("Rank 4")
plt.show()
 
# One-hot encoding the rank
# Make dummy variables for rank
one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)
 
# Drop the previous rank column
one_hot_data = one_hot_data.drop('rank', axis=1)
 
# Print the first 10 rows of our data
one_hot_data[:10]
 
# Scaling the data
# Copying our data
processed_data = one_hot_data[:]
 
# Scaling the columns
processed_data['gre'] = processed_data['gre']/800
processed_data['gpa'] = processed_data['gpa']/4.0
processed_data[:10]
 
# Splitting the data into Training and Testing
sample = np.random.choice(processed_data.index, size=int(len(processed_data)*0.9), replace=False)
train_data, test_data = processed_data.iloc[sample], processed_data.drop(sample)
 
print("Number of training samples is", len(train_data))
print("Number of testing samples is", len(test_data))
print(train_data[:10])
print(test_data[:10])
 
# Splitting the data into features and targets (labels)
import keras
 
# Separate data and one-hot encode the output
# Note: We're also turning the data into numpy arrays, in order to train the model in Keras
features = np.array(train_data.drop('admit', axis=1))
targets = np.array(keras.utils.to_categorical(train_data['admit'], 2))
features_test = np.array(test_data.drop('admit', axis=1))
targets_test = np.array(keras.utils.to_categorical(test_data['admit'], 2))
 
print(features[:10])
print(targets[:10])
 
# Defining the model architecture
# Imports
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
 
# Building the model
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(6,)))
model.add(Dropout(.2))
model.add(Dense(64, activation='relu'))
model.add(Dropout(.1))
model.add(Dense(2, activation='softmax'))
 
# Compiling the model
model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
 
# Training the model
# Training the model
model.fit(features, targets, epochs=200, batch_size=100, verbose=0)
 
# Scoring the model
# Evaluating the model on the training and testing set
score = model.evaluate(features, targets)
print("\n Training Accuracy:", score[1])
score = model.evaluate(features_test, targets_test)
print("\n Testing Accuracy:", score[1])