Made model using keras :P

Author: adhyans

.gitignore

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+.ipynb_checkpoints

student-admissions-keras/StudentAdmissions.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import keras
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.utils import np_utils
+
+data = pd.read_csv('student_data.csv')
+
+def plot_points(data):
+    X = np.array(data[['gre', 'gpa']])
+    y = np.array(data['admit'])
+    admit =  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], color = 'red')
+    plt.scatter([s[0][0] for s in admit], [s[0][1] for s in admit], color = 'cyan')
+
+# Plotting the points
+plot_points(data)
+# plt.show()
+
+#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]
+
+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()
+
+# Make dummy variables for rank
+one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)
+
+one_hot_data = one_hot_data.drop('rank', axis=1)
+
+processed_data = one_hot_data[:]
+processed_data['gre'] = processed_data['gre']/800
+processed_data['gpa'] = processed_data['gre']/4.0
+processed_data[:10]
+
+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])
+
+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])
+
+
+model = Sequential()
+model.add(Dense(128, activation='sigmoid', input_shape=(6,)))
+model.add(Dropout(.2))
+model.add(Dense(64, activation='sigmoid'))
+model.add(Dropout(.1))
+model.add(Dense(2, activation='sigmoid'))
+
+model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+model.summary()
+
+model.fit(features, targets, epochs=200, batch_size=100, verbose=0)
+
+score = model.evaluate(features, targets)
+print("\n Training Accuracy:", score[1])
+score = model.evaluate(features_test, targets_test)
+print("\n Testing Accuracy:", score[1])