This GitHub repository presents an assignment focused on building a multiple linear regression model to accurately predict the demand for shared bikes. The assignment focuses on understanding the factors affecting bike demand, evaluating the model's performance using R-squared score, and providing insights for business strategy and market dynamics.
day.csv have the following fields:
- instant: record index
- dteday : date
- season : season (1:spring, 2:summer, 3:fall, 4:winter)
- yr : year (0: 2018, 1:2019)
- mnth : month ( 1 to 12)
- holiday : weather day is a holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
- 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 : temperature in Celsius
- atemp: feeling temperature in Celsius
- hum: humidity
- windspeed: wind speed
- casual: count of casual users
- registered: count of registered users
- cnt: count of total rental bikes including both casual and registered
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent