Bike sharing demand

Linear regression

Contents

Kaggle Project #1 - Bike Sharing Demand

Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.

The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.

  • datetime - hourly date + timestamp
  • season - 1 = spring, 2 = summer, 3 = fall, 4 = winter
  • holiday - whether the day is considered a holiday
  • workingday - whether the day is neither a weekend nor holiday
  • weather
    • 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 - temperature in Celsius
  • atemp - "feels like" temperature in Celsius
  • humidity - relative humidity
  • windspeed - wind speed
  • casual - number of non-registered user rentals initiated
  • registered - number of registered user rentals initiated
  • count - number of total rentals

1ni=1n(log(pi+1)log(ai+1))2 \sqrt{\frac{1}{n} \sum_{i=1}^n (\log(p_i + 1) - \log(a_i+1))^2 }

  • n is the number of hours in the test set
  • pi is your predicted count
  • ai is the actual count
  • log(x) is the natural logarithm"
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