Bike Sharing Dashboard

Tugas Akhir Proyek Analisis Data

Setup environment

conda create --name projek1 python=3.11
conda activate projek1
pip install numpy pandas matplotlib seaborn jupyter streamlit

streamlit run dashboard.py

Both hour.csv and day.csv have the following fields, except hr which is not available in day.csv:

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 DC Government 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: Normalized temperature in Celsius. The values are divided by 41 (max)
atemp: Normalized feeling temperature in Celsius. The values are divided by 50 (max)
hum: Normalized humidity. The values are divided by 100 (max)
windspeed: Normalized wind speed. The values are divided by 67 (max)
casual: count of casual users
registered: count of registered users
cnt: count of total rental bikes including both casual and registered

Information about main_data.csv:

date: date format yyyy-mm-dd
season: category (spring, summer, fall, winter)
year: category (2011, 2012)
month: category ('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')
hour: int (0 to 23)
holiday: category, day is holiday or not
weekday: category, day of the week ('Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday')
workingday: category, if day is neither weekend nor holiday is YES, otherwise is No.
weather: category (Clear, Misty, Light_RainSnow, Heavy_RainSnow)
temp: float, temperature in Celsius. The values are multiplied by 41 (max)
atemp: float, feeling temperature in Celsius. The values are multiplied by 50 (max)
humidity: float, humidity. The values are multiplied by 100 (max)
windspeed: float, wind speed. The values are multiplied by 67 (max)
casual: count of casual users
registered: count of registered users
total_count: count of total rental bikes including both casual and registered