- Perform exploratory data analysis and visulize the data to understand the environment and sessional settings.
- Predict bike rental counts based on environmental and seasonal settings with the help of a machine learning algorithm.
In bike-sharing systems, the entire process from membership to rental and return has been automated. Using these systems, users can easily rent a bike from one location and return it to another.Hence, a bike rental company wants to understand and predict the number of bikes rented daily based on the environment and seasons.
In this section, we will perform exploratory data analysis (EDA) on the dataset to gain insights and understand the data better.
The dataset contains information about bike rentals, including various environmental and seasonal settings. Here are the variables in the dataset:
instant: Record Indexdteday: Dateseason: Season (1: Spring, 2: Summer, 3: Fall, 4: Winter)yr: Year (0: 2018, 1: 2019)mnth: Month (1 to 12)holiday: Weather Day is holidayweekday: Day of the weekweathersit: Weather situation (1: Clear, few clouds, partly cloudy, 2: Mist + cloudy, 3: Light snow, 4: Heavy rain)temp: Normalized temperature in Celsiusatemp: Normalized feeling temperature in Celsiushum: Normalized humiditywindspeed: Normalized wind speedcasual: Count of casual usersregistered: Count of registered userscnt: Count of total rental bikes including both casual and registered
Before we proceed with the analysis, we will clean the data by removing unnecessary columns and handling missing values, if any.
We will visualize the data using various plots and charts to understand the relationships between different variables and the target variable (cnt).
We will perform statistical analysis on the dataset to identify any significant patterns or trends.
Finally, we will develop a machine learning model to predict the number of bike rentals based on the given features.
Let's get started with the exploratory data analysis!