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Some of the cleaned code from the internships in Labs

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Some of the cleaned code from the internships in Labs

  • Eccentricity fit.py - The data provided in pandas.Dataframe was visualized and then logistic and polynomial regression was fitted to the mean eccentricity values.

  • Majority-Minority Model.py - Create a Watts-Strogatz small-world graph using NetworkX and then track the changes in the network and check the frequency and speed with which the network reaches a desicion or a stalemate with different initialization parameters:

  1. Number of nodes
  2. Number of connections between nodes
  3. Proportion of nodes with the same initial values
  4. Probability of rewiring
  5. Probability of a change in the value of a node
  6. Vote update methods - synchronous and asynchronous (whether all nodes updates at the same time or sequentially)

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