Finished Data Cleaning

Devlog 4
Previously, I’d cleaned the Global Terrorism Dataset which gave me information of past instances of terrorism in a country. I’ve merged two new datasets in, the Uppsala University Georeferenced Events Dataset, from which I calculated the civilian death toll of a conflict, and the UN’s Human Development Index, which I used to asses the living standard of the country. I hope these metrics will be sufficient to give me an accurate analysis.

My learning:
The right dataset matters more than how you clean it
Perhaps this isn’t true for big data, but I’ve found that the right dataset can save like half the cleaning. I tried using ACLED, the One Sided events database from Uppsala, and looked at several other metrics (I originally planned to use GNI for economic situation, but I found HDI is a more accurate metric). The UN dataset was wonderfully maintained, and I didn’t have to clean it too much. The merge did take some trial and error, with column renaming and all that, but overall the work was easy.

About TerrorPredictor:
It’s an ML model that uses publically available data about factors relating to conflict, in order to the number of terrorist attacks for a given country in a year.