Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
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Creating predictive models with Keras

Our data now contains the following columns:

type_TRANSFER, isNight

Now that we've got the columns, our data is prepared, and we can use it to create a model.

Extracting the target

To train the model, a neural network needs a target. In our case, isFraud is the target, so we have to separate it from the rest of the data. We can do this by running:

y_df = df['isFraud']
x_df = df.drop('isFraud',axis=1)

The first step only returns the isFraud column and assigns it to y_df.

The second step returns all columns except isFraud and assigns them to x_df.

We also need to convert our data from a pandas DataFrame to NumPy arrays. The pandas DataFrame is built on top of NumPy arrays but comes with lots of extra bells and whistles that make all the preprocessing we did earlier possible. To train a neural network, however, we just need the underlying data...