class_docstr: Electricity prices in New South Wales.
  
  This is a binary classification task, where the goal is to predict if the price of electricity
  will go up or down.
  
  This data was collected from the Australian New South Wales Electricity Market. In this market,
  prices are not fixed and are affected by demand and supply of the market. They are set every
  five minutes. Electricity transfers to/from the neighboring state of Victoria were done to
  alleviate fluctuations.
  
  References
  ----------
  [^1]: [SPLICE-2 Comparative Evaluation: Electricity Pricing](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.9405)
  [^2]: [DataHub description](https://datahub.io/machine-learning/electricity#readme)
code: def _iter(self):
      return stream.iter_csv(
          self.path,
          target="class",
          converters={
              "date": float,
              "day": int,
              "period": float,
              "nswprice": float,
              "nswdemand": float,
              "vicprice": float,
              "vicdemand": float,
              "transfer": float,
              "class": lambda x: x == "UP",
          },
      )
  
imports: from __future__ import annotations
  from river import stream
  from .None import base
label_desc: Use image node_id 2 for calling the Elec2 obj's underlying member method code with example usage: obj._iter() and returns: stream