Neural networks

This is an assignment to be done in Python. I need the solution as an PDF report including code, output, descriptions / comments, graphs with captions and conclusions. I’m attaching the lecture file as well for your reference if required go through it.

The data set to be used to work upon is attached as well as the lecture file for reference.

scenario. The bank has been seen unusual churn rates for their customers (churn is when people leave the company). They want to understand the problem. Therefore, they hired you, as a data scientist, to look into their data set and give them insights / solutions. Six months ago the bank took a sample of their customers and measured some characteristics about their customers, such as credit score, geography, gender, age, tenure (how many years a customer is with the bank), balance, number of products a customer had with the bank, if he had a credit card, if he is an active member, estimated salary (the bank has estimated the salary based of the data they had), exited (if a customer has left the bank or not within the last six months). Your task is to create a classification model to tell the bank which of the customers are at the highest risk of leaving.

For this task you will be using Artificial Neural Networks ANN and Keras library that wraps Tensorflow and Theano libraries. Keras library lets you build deep learning models in a few lines of code. Keras was developed by François Chollet a machine learning scientist at Google. First, you have to install all three libraries. The easiest way to do this is to use terminal and the following lines of code:

# Install Theano

# pip install –upgrade –no-deps git+git://github.com/Theano/Theano.git

# Install Tensorflow

# Install Tensorflow from the website: https://www.tensorflow.org/versions/r0.12/get_star…

# Install Keras

# pip install –upgrade keras

Before building ANN encode categorical variables, scale the features (normalize the X values using one of the algorithms discussed in the earlier classes – Min-Max, Z-score or decimal scaling), split data into training and test sets. Then import Keras, import Sequential module from keras.models to initialize the neural network, import Dense from keras.layers to create layers in the ANN. Then initialize your ANN as a sequence of layers (another option to initialize ANN is by defining a graph). Add the input layer, the hidden layer or several layers (I do not recommend using more than two-three layers for this task), and the output layer. For hidden layers choose any of the activation functions discussed in class (sigmoid, threshold, rectifier, hyperbolic tangent) or you can try several of them and compare them. For the output layer use sigmoid activation function, as you are interested in probabilities of a customer to leave the bank. Then compile your ANN, id est., apply stochastic gradient descent (or any other optimizer, or use several of them and compare the output) on your ANN. Fit your ANN to the training set and predict the test set values (getting 0 and 1 as the prediction result). Finally, use accuracy, loss, sensitivity and specificity to evaluate your model. If you wish, additionally you can support your decision with lift or gain charts, also with a calibration chart if you are comparing several different architectures or optimizers for the ANN. Make the report including your code, output, descriptions/comments, graphs with captions and conclusions.

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