Prediction using classification algorithms

These codes were used during the stage C of Hamoye Data science Internship

Electrical Grid Stability Simulated Data Data Set

Data Set Information:

The analysis is performed for different sets of input values using the methodology similar to that described in [Schäfer, Benjamin, et al. 'Taming instabilities in power grid networks by decentralized control.' The European Physical Journal Special Topics 225.3 (2016): 569-582.]. Several input values are kept the same: averaging time: 2 s; coupling strength: 8 s^-2; damping: 0.1 s^-1

Attribute Information:

11 predictive attributes, 1 non-predictive(p1), 2 goal fields:

1. tau[x]: reaction time of participant (real from the range [0.5,10]s). Tau1 - the value for electricity producer.

2. p[x]: nominal power consumed(negative)/produced(positive)(real). For consumers from the range [-0.5,-2]s^-2; p1 = abs(p2 + p3 + p4)

3. g[x]: coefficient (gamma) proportional to price elasticity (real from the range [0.05,1]s^-1). g1 - the value for electricity producer.

4. stab: the maximal real part of the characteristic equation root (if positive - the system is linearly unstable)(real)

5. stabf: the stability label of the system (categorical: stable/unstable)

Link to dataset: https://archive.ics.uci.edu/ml/datasets/Electrical+Grid+Stability+Simulated+Data+#