Understanding World Population

This project is aimed to do an analysis of how human population is spread all around the globe. For this analysis the following dataset is used:

Dataset: Population by Country - 2020 (Source - Kaggle)
. This dataset provides infomation about country wise population, population density, yearly change, fertility rate, median age et cetera.

Along with the python programming language, following tools are used for this:

• Numpy
• Pandas
• Matplotlib
• Seaborn

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml. You can run and experiment with the code in a couple of ways: using free online resources or on your own computer.

Option 1: Running using free online resources

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade

3. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:

jovian clone notebook-owner/notebook-id

4. Enter the newly created directory using cd directory-name and start the Jupyter notebook.

jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

As mentioned above the dataset for analysis has been downloaded from kaggle.

Let's begin by downloading the data, and listing the files within the dataset.