Numerical Methods: "Life Expectancy and Linear Regression" Group Project

Made by Oscar Álvarez, Andrés Barbuto, Sebastián Bocaccio and Martín Mallol, 2021, 2nd semester

Local Setup

1. Create an empty git repo and clone this project

git init
git remote add origin <new-git-url>

2. Download the pybindandeigen` repos as submodules

git submodule init
git submodule add https://github.com/eigenteam/eigen-git-mirror
git submodule add https://github.com/pybind/pybind11
git mv eigen-git-mirror eigen
# We choose the versions of eigen and pybind
cd pybind11/ && git checkout v2.2.4 && cd ..
cd eigen && git checkout 3.3.7 && cd ..

3. Install requirements (Previously, activate the virtual environment in your PC. See below.)

pip install -r requirements.txt

4. Decompress data

cd data && gunzip *.gz && cd ..

5. Run Jupyter

jupyter lab

That's it!

Python's virtual environment creation

With Python3 (this one worked best for me)

python3 -m venv your-project-name
source your-project-name/bin/activate

With pyenv

curl https://pyenv.run | bash

Then, it is suggested to add some code lines into the basrch. After that, RESTART THE SHELL and add the following code...

pyenv install 3.6.5
pyenv global 3.6.5
pyenv virtualenv 3.6.5 your-project-name

On the project's directory:

pyenv activate your-project-name

With Conda

conda create --name your-project-name python=3.6.5
conda activate your-project-name

Dependencies installment

pip install -r requirements.txt

How to run Jupyter Notebooks

1st way

cd notebooks
jupyter lab

2nd Way

jupyter notebook

Compilation

Execute the first cell of the cm.ipynb notebook.

Overview

This project tries to analise a vast dataset provided by the World Health Organization. It attempts to compare a country's life expectancy with other attributes, searching for a correlation and explanation on why life expectancy on that country/region has that value.

First, an exploratory analiis is made, looking for outliers or curious cases on the dataset. Then, we add new info to the provided dataset by attaching attributes such as: suicide rates, viruses, geographic location, deseases, etc. With all this information on the palm of our hand, we made multiple experiments searching for a reason on why these characteristics may impact life expectancy on a specific country or region.

Project's directories and files

In data/ one can find the principal dataset and all the .csv files added and utilised on the project.

In notebooks/ reside all the experiments we made.

In src/ C++ code can be found. There, we implemented the Least Square Method.

Finally, Informe.pdf is the essay.

Users should be able to

• Compile and run the C++ code.
• Run every Notebook in the project (experiments and testing).
• Read the essay and compare our conclusions with theirs.

Built with

• C++
• Python
• Jupyter Notebooks
• Life Expectancy WHO (World Health Organization) dataset

What I learnt

• Add new information to an existent dataset
• Find outliers, and similarities on different attributes of an object.
• Work with linear regression.
• Apply studentized Residuals, H Leverage, Cook Distance, influence plots.
• Utilise metrics such as $R^2$, $R^2$ adjusted and RMSE.