Analyzing The Credit Worthiness of Nigeria

This is a simple data analysis problem built good for anyone looking to get up and running with data analysis using Python. Here we are using a hypothical senerio which can be found below.

Senerio:

You are the secretary general of the United Nations (UN) and you just got a request from World Bank seeking that you consider if Nigeria should be granted a $1bn loan to fund the Economic Recovery Growth Plan of the Nigerian government as a way to help the country recover from economic recession. You have also been provided with the following data sets mostly from World Bank:

GDP data of countries of the world (GDP.csv) – source: World Bank (https://data.worldbank.org)
External debt profile of different countries (debt.csv) – source: World Bank (https://data.worldbank.org)
Budget of data for Nigeria only(in Naira) (budget.csv) –
Foreign Reserve (Foreign_reserve.csv) – source: World Bank (https://data.worldbank.org)

Questions:

a. Based on this data recommend to the World Bank if the loan should be granted and why?
b. If it will how much in USD should it be?
c. If not what do you recommend as next line of action to the World Bank.

Constraints:

The loan must be repaid with in 10 years

Assumptions:

Extra budgetary spending not considered
Extra budgetary income(grants, gifts etc) not consider

Hypothesis:

Nigeria cannot pay back $1bn in 10 years

How to view this project

1. Download and extract this repo to you computer.

2. Download the appropriate version of Anaconda on you computer. https://www.anaconda.com/download/

3. Install Anaconda on your computer.

4. After the install launch 'Jupyter Notebook'. N/B: This will start the Jupyter server and also automatically launch you default browser. Please avoid shutting down the server (Jupyter Commandline)

5. From the browser locate the folder you extracted and open the 'Nigeria's Credit Worthiness.ipynb'

6. Enjoy!

Final Words:

1. Feel free to adapt this project for any country of your choice. You can obtain the data sets you need from https://data.worldbank.org.

2. I have written most of the codes in pure Python 3 without too many external libraries except a few necessary ones.

3. If you need help in your project kindly send an email. Goodluck in your analysis.