Predicting Gender using name sound

Analyze the gender distribution of children's book writers and use sound to match names to gender

The same name can be spelled out in a many ways (for example, Marc and Mark, or Elizabeth and Elisabeth). Sound can, therefore, be a better way to match names than spelling. In this project, we will use the Python package Fuzzy to find out the genders of authors that have appeared in the New York Times Best Seller list for Children's Picture books.

First, using fuzzy (sound) name matching, we will search for author names in a dataset provided by the US Social Security Administration that contains names and genders of all individuals who have applied for Social Security Cards. Next, we'll aggregate the author dataset by including gender. Finally, we will use the new dataset to plot the gender distribution of children's picturebooks authors over time.

To complete this project, we should be familiar with pandas DataFrames, NumPy for basic statistics, and Matplotlib for plotting.

Project Tasks:

1. Sound it out!

Import the fuzzy library and checking its working

2. Authoring the authors

Load the Datasets and storing the author first names

3. It's time to bring on the phonics... again

Create fuzzy equivalent for the first names

4. The inbetweeners

Load the baby names dataset and their genders

5. Playing matchmaker

Determining gender of authors by name sounds

6. Tally up

Determining number of respective genders of authors per year

7. Foreign-born authors

Checking number of authors names were not matched.

8. Raising the bar

Comparing the authors as number of male or female vs. year - bar chart