Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.
- Predicting the Bike Sharing Demand through a Random Forest Regressor Model
- EDA (Exploratory Data Analysis)
- Data Processing
- Data Transformation
- Python
- Pandas Library
- Numpy Library
- Matplotlib.pyplot Library
- Seaborn Library
- Sklearn Library
You can find the datasets used in this project through the following Kaggle's Link: https://www.kaggle.com/competitions/bike-sharing-demand
| Column | description |
|---|---|
| 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 |
To check full project either check Bike Sharing Demand - Random Forest Regressor.ipynb in the repository or Bike Sharing Demand - Random Forest Regressor.ipynb