使用Keras和TensorFlow获取观众见解

最近我发现了Keras和TensorFlow,我正试图进入ML.我手动将来自用户DB的列车和测试数据分类如下:

9个功能和标签,功能是我的系统中的事件,如"用户添加了个人资料图片"或"用户付费X服务",标签是正或负ROI(1或0)

样品:

我使用以下代码对用户进行了分类:

import numpy as np
from keras.layers import Dense
from keras.models import Sequential

train_data = np.loadtxt("train.csv", delimiter=",", skiprows=1)
test_data = np.loadtxt("test.csv", delimiter=",", skiprows=1)

X_train = train_data[:, 0:9]
Y_train = train_data[:, 9]

X_test = test_data[:, 0:9]
Y_test = test_data[:, 9]

model = Sequential()
model.add(Dense(8, input_dim=9, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(3, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Fit the model
model.fit(X_train, Y_train, epochs=12000, batch_size=10)

# evaluate the model
scores = model.evaluate(X_test, Y_test)
print("\n\n\nResults: %s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

并获得89%的准确率.这很有用,可以将用户标记为有价值的客户.

问:如何提取为可能的投资回报率做出贡献的功能,以便提升他们对用户体验的关注度？

或者:找到最佳组合观众群的方法是什么？