Book Image

Neural Network Projects with Python

By : James Loy
Book Image

Neural Network Projects with Python

By: James Loy

Overview of this book

Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
Table of Contents (10 chapters)

Data preprocessing

Recall from the previous project that we had to preprocess the data by removing missing values and other data anomalies. In this project, we'll perform the same process. We'll also perform feature engineering to improve both the quality and quantity of the features before training our neural network on it.

Handling missing values and data anomalies

Let's do a check to see whether there are any missing values in our dataset:

print(df.isnull().sum())

We'll see the following output showing the number of missing values in each column:

We can see that there are only five rows (out of 500,000 rows) with missing data. With a missing data percentage of just 0.001%, it seems that we don&apos...