Project - Forecast Bike Rentals

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End to End Project - Bikes Assessment - Basic - Cleaning the data - Dropping unwanted features

As we can see, the bikesData data set contains some features which may not be needed for this problem e.g.

  1. instant - This is just an index holder.
  2. casual - It contains the count of casual bike riders, which are already included in the overall users count i.e. 'cnt', hence not needed.
  3. registered - It contains the registered bike riders, which again, like the casual riders, are already included in the overall users count i.e. 'cnt', hence not needed.
  4. atemp - It is the 'feel' temperature, which may not be needed, as we already have 'temp' feature which contains temperature of the area - duplicate feature, hence not needed.
  5. dteday - It is the Date. Since, our prediction is not based on the Date, its based on hour of the day, hence Date is not needed.

Since, these features are not needed for our current problem, let us drop them from the bikesData data set.

INSTRUCTIONS

  • Please define a python list called columnsToDrop to store the above-mentioned feature(column) names which needs to be dropped from bikesData data set.

  • Please drop these unwanted features (columns) defined by columnsToDrop variable above from the bikesData dataframe and store the resulting dataframe in bikesData dataframe variable.

Hint: you can use the drop function with axis=1 as the second argument.

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