Seoul.city.s.bike.sharing.service.offers.the.greatest.satisfaction.to.its.users.mp4
Source: Arirand
Task: Predicting Count of bikes rented at each hour(Rent Bike Count)
Dataset: UCI Machine Learning
Complete JupyterNotebook: Link
Metrics:
| Algorithm | MAE | MSE | RMSE | R2 |
|---|---|---|---|---|
| Linear Regression | 318.88402 | 178535.387537 | 422.534481 | 0.560321 |
Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes. The dataset contains weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall), the number of bikes rented per hour and date information.
Date : year-month-day
Rented Bike count - Count of bikes rented at each hour
Hour - Hour of he day
Temperature-Temperature in Celsius
Humidity - %
Windspeed - m/s
Visibility - 10m
Dew point temperature - Celsius
Solar radiation - MJ/m2
Rainfall - mm
Snowfall - cm
Seasons - Winter, Spring, Summer, Autumn
Holiday - Holiday/No holiday
Functional Day - NoFunc(Non Functional Hours), Fun(Functional hours)