This is a final project for MDA course in KU LEUVEN (2021)
In a nutshell, we want you to think as a data-scientist throughout the whole production pipeline: retrieving & pre-processing data, exception handling, building a model, hyperparameter optimization, etc... We expect that you bring the topics explained during the course into practice. Your team should be able to bring value to the data. You can use techniques that were not covered during the course and can bring other python packages into the project. Make sure you start from the same python environment, used in the course. Of course you can update packages, install new ones,... Make sure that you understand the underlying mathematics in the approach that you use (supervised, unsupervised, nlp, AI,..). A data-scientist is much more than an expert in Sklearn, NLTK, Pytorch,etc...
The European Central Bank has been buying corporate bonds since 2015. This signifies an important cash injection in the European Economy. The ECB started buying assets from commercial banks as part of its non-standard monetary policy measures. These asset purchases, also known as quantitative easing or QE, support economic growth across the euro area and helps Europe to return to inflation levels below, but close to, 2%.
Has the European Central bank been supporting the green economy when purchasing corporate bonds ?
Data1: https://www.ecb.europa.eu/mopo/implement/app/html/index.en.html#cspp
Data2: https://sdw.ecb.europa.eu
To find information of a sector / industry a company is belonging to, you might for example want to consult the Refinitiv database (API-available) https://permid.org/
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`