Rental Bike Data Analysis
This project involves analyzing a dataset containing rental bike details to gain insights into various factors influencing bike rentals. The dataset includes information such as datetime, season, holiday, working day, weather, temperature, humidity, wind speed, and rental counts. The analysis focuses on understanding the relationships between different variables and rental counts.
Objective The objectives of this project are to:
Analyze the relationship between working days and rental counts.
Analyze the relationship between seasons and rental counts.
Analyze the relationship between weather conditions and rental counts.
Analyze the relationship between holidays and rental counts.
Derive meaningful insights from the dataset.
Features
Working Day Analysis: Analyzing how rental counts vary on working days versus non-working days.
Seasonal Analysis: Analyzing how rental counts vary across different seasons.
Weather Analysis: Analyzing how rental counts vary under different weather conditions.
Holiday Analysis: Analyzing how rental counts vary on holidays versus non-holidays.
Data Insights: Extracting meaningful insights from the dataset.
Dataset
The dataset includes the following columns:
Datetime: Timestamp of the rental.
Season: Season of the year (1: Winter, 2: Spring, 3: Summer, 4: Fall).
Holiday: Whether the day is a holiday (1: Yes, 0: No).
Working Day: Whether the day is a working day (1: Yes, 0: No).
Weather: Weather condition (1: Clear, 2: Mist, 3: Light Snow/Rain, 4: Heavy Rain/Ice Pallets/Snow).
Temp: Temperature in Celsius.
Atemp: "Feels-like" temperature in Celsius.
Humidity: Humidity percentage.
Windspeed: Wind speed.
Casual: Number of casual users (non-registered).
Registered: Number of registered users.
Count: Total number of bike rentals (casual + registered).
Methodology
Data Cleaning: Handling missing values and outliers.
Exploratory Data Analysis (EDA): Visualizing the relationships between different variables and rental counts.
Analysis of Working Day vs Count: Understanding how rental counts vary on working days versus non-working days.
Analysis of Season vs Count: Understanding how rental counts vary across different seasons.
Analysis of Weather vs Count: Understanding how rental counts vary under different weather conditions.
Analysis of Holiday vs Count: Understanding how rental counts vary on holidays versus non-holidays.
Insights: Deriving meaningful insights from the dataset.
Results
The analysis revealed the following insights:
Rental counts are generally higher on working days compared to non-working days.
Rental counts vary significantly across different seasons, with the highest counts observed in certain seasons.
Weather conditions have a noticeable impact on rental counts, with clear weather leading to higher rentals.
Holidays see a variation in rental counts, often differing from regular days.
Usage
To use this project, follow these steps:
Clone the repository:
git clone https://github.com/lm934/Case-Study-Rental-Bike-Data-Analysis.git
Conclusion
This project provides a detailed analysis of rental bike data, uncovering important insights into how various factors affect bike rentals. The analysis helps in understanding user behavior and can aid in making data-driven decisions to optimize rental operations.