This project is about analyzing a dataset of protests and building simple model to predict a successful protest.
Using python (NumPy, Pandas, Matplotlib, sklearn ..), trying to find factors which make protests achieve their final goals.
Datasets used:
- 'NAVCO 2.1 Dataset' - Nonviolent and Violent Campaigns and Outcomes
- 'World Population 1960-2018' - Population of each country, 1960-2018
00. Presentation.pptx
The projects presentation.
01. ViolenceAndSuccess.ipynb
Main objective : See whether violence contributes to the success rate of protests.
In this notebook we see the relationship between violent / non-violent protests and their success.
02. SizeAndSuccess.ipynb
Main objective : See whether larger protests are more likely to succeed.
In this notebook we see the relationship between the size of a protest and its success.
Comparing the protests in the dataset with the George Floyd and BLM movement worldwide protests to see differences.
03. ProtestSuccessPrediction.ipynb
Main objective : Build a model to predict a successful protest.
In this notebook we see an attempt to build a model which predict if a protest was successful or failed.
Using logistic regression, predicting the success of a protest.
04. Variables.pdf
Information on used variables.