Hackathon: Gitcoin Open Data Science Hackathon : Sybil Slayer
The data created for the analysis is available here on Hugging Face
The report is in the github repo A time series analysis of gitcoin grants contributors to Grant 15
To run the jupyter notebook please put the features datasets and the hackathon-contributions-dataset_v2.csv as shown in the directory and file structure below:
data
+----features
+----- eth_polygon
+----- features_eth_polygon.csv
+----- eth_std
+----- features_eth_std.csv
+----- hackathon-contributions-dataset_v2.csv
Use requirements to install the libraries:
pip install requirements.txt
The main jupyter notebook used to create the report is k_mean_eth. Be careful before running it is memory heavy because it automatically exports more than 150 charts.
The k_mean_polygon notebook is similar and produces the results for the polygon transactions.
The agglomerative_eth file shows the results in the case of the agglomerative clustering.
An example of clustering for Ethereum is provided in eth_std_clusters.csv, please mind that it was added after the report writings and thus the seed used for K-means was not the same, thus cluster names are different from the one used in the report.
For Etherscan run src\main\extract_etherscan_txs.py
For Polygon chain run src\main\extract_polygon_txs.py
Parameters are hard coded and should be modified!
run src\main\features\create_feature_df.py
Parameters are hard coded and should be modified! You can change the tx_chain parameter to select the network.