This dataset contains information on 1,000 individuals from diverse backgrounds, along with details about whether they purchased a bike. It is suitable for building prediction models using machine learning algorithms. The dataset includes some missing (NA) values, making it ideal for data cleaning, exploration, and visualization exercises.
Kaggle Dataset Available here :
Processed Dataset : https://github.com/slidescope/data/blob/master/Bike-Buyers.xlsx
Columns:
- ID: Unique identifier for each individual.
- Marital Status: Marital status of the individual.
- Gender: Gender of the individual.
- Income: Annual income of the individual.
- Children: Number of children.
- Education: Education level of the individual.
- Occupation: Type of occupation.
- Home Owner: Indicates whether the individual owns a home.
- Cars: Number of cars owned.
- Commute Distance: Distance traveled to work.
- Region: Geographic region of residence.
- Age: Age of the individual.
- Purchased Bike: Indicates whether the individual purchased a bike.
This dataset is perfect for practicing and applying techniques such as data cleaning, exploratory data analysis (EDA), and visualization.