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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.