henrypaik1 / bikesharingdemand

kaggle Bike Sharing Demand Data Analysis: 회귀분석을 통한 자전거 수요 예측

Bike Sharing Demand: by linear regression

• Kaggle: https://www.kaggle.com/c/bike-sharing-demand
• Result

Data Fields

• 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

Key concept: Interaction between features and create new category features

• eg. re-categorized hour category * temperature
• x axis: temperature
• y axis: log transformed count(y)

a) No Interaction and No Category

b) Interaction and Category