This project designs a webpage which helps people decide which country is the best to migrate to. The webpage has a dashboard and a map that display data for countries and cities that are relevant to making migration decisions.
There are 5 stages to this project:
- We located the data sources and cleaned the dataset with Python and Pandas.
- We wrote the dataset into a SQLite database.
- We set up the Flask API to run the webpage.
- We downloaded and modified a bootscrap template and then designed the dashboard on that basis. The dashboard has a scatter plot and a radar plot that show various indices for countries in each continent, as well as definitions to the various indices.
- We also designed an interactive map using Leaflet that allows the user to further explore the data for each country and city.
Data sources:
- United Nations Database (HDI)
- Numbeo
File Structure:
- dataset -> Contains all the CSV files used to curate our dataset
- output -> output files from the data cleaning/processing stage (from cleaning.ipynb)
- static -> contains bootstrap plugins + geojson data (data)
- templates -> html files for website structure
- app.py = python code that contains Flask routes
- cleaning.ipynb = jupyter notebook containing data cleaning process
- schema.py = python code to create the SQLite database.
Instructions to run the programme:
- Activate your virtual environment
- Pip install flask_cors module
- In Git Bash, cd to the directory in which app.py file is located on your local computer
- Enter "flask run" or python app.py" to run the flask