This project is slightly different than others you have encountered thus far on Codecademy. Instead of a step-by-step tutorial, this project contains a series of open-ended requirements which describe the project you’ll be building. There are many possible ways to fulfill all of these requirements correctly, and you should expect to use the internet, Codecademy, and other resources when you encounter a problem that you cannot easily solve.
Congratulations! You are the newest member of the data science team at Kiva!
“More than 1.7 billion people around the world are unbanked and can’t access the financial services they need. Kiva is an international nonprofit, founded in 2005 in San Francisco, with a mission to expand financial access to help underserved communities thrive.”
As a data analyst, you are asked to perform light exploratory data analysis on this Kaggle dataset, which contains information about loans awarded by the non-profit Kiva.
In this project you’ll visualize insights using Seaborn, you’ll explore the average loan amount by country using aggregated bar charts. You’ll also visualize the distribution of loan amounts by project type and gender using box plots and violin plots.
Use your knowledge of EDA and Seaborn to create informative visualizations for the Kiva marketing team.
You have two options for completing this assignment. Either here, within Codecademy’s output terminal, or on your own, in case you’re more comfortable using a Jupyter notebook.
If you choose to do this project on your computer instead of Codecademy, you can download what you’ll need by clicking the “Download” button below. If you need help setting up your computer, be sure to check out our setup guides:
Download the Seaborn_Kiva zipped folder.
Open kiva_project.ipynb and follow the steps in the Jupyter Notebook. If you get stuck, you can look at kiva_solution.ipynb for the answer.