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Predict the bike demand in future

You are required to build the model which estimates the bike demand in future given the parameters as observed in the past. You can either use BootML or AzureML for the same. Please note this project is already part of the BootML under "Bike Assessment" so you can continue from there.

Read more about the dataset here. Use the day.csv file to train the model.

Features in the dataset -

  • instant: record index

  • dteday : date

  • season: season (1: springer, 2: summer, 3: fall, 4: winter)

  • yr: year (0: 2011, 1:2012)

  • mnth: month (1 to 12)

  • hr: hour (0 to 23)

  • holiday: weather day is holiday or not (extracted from [Web Link])

  • weekday: day of the week

  • workingday: if day is neither weekend nor holiday is 1, otherwise is 0.

  • weathersit:

    1: Clear, Few clouds, Partly cloudy, Partly cloudy

    2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist

    3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds

    4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog

  • temp: Normalized temperature in Celsius. The values are divided to 41 (max)

  • atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)

  • hum: Normalized humidity. The values are divided to 100 (max)

  • windspeed: Normalized wind speed. The values are divided to 67 (max)

  • casual: count of casual users

  • registered: count of registered users

  • cnt: count of total rental bikes including both casual and registered

After you have trained the model, document the RMSE and your learnings here. Be as detailed as possible while documenting your learnings.

All the best and happy learning!


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