View Code? Open in Web Editor
NEW
kaggle Bike Sharing Demand Data Analysis: 회귀분석을 통한 자전거 수요 예측
License: MIT License
bikesharingdemand's Introduction
Bike Sharing Demand: by linear regression
![](result.png)
- 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
![](bike1.png)
b) Interaction and Category
![](bike2.png)
bikesharingdemand's People
Contributors
Stargazers
Watchers