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Customer Purchase Prediction Data
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About
Information about users is provided to predict whether they will purchase a product. The data contains four input features—User ID, Gender, Age, and Estimated Salary—which are used to determine if a user made a purchase. This is indicated by the 'Purchased' column, making it suitable for binary classification tasks.
Columns
- User ID: A unique identifier assigned to each user. It is used to track individual user information and is not expected to have predictive power.
- Gender: The user's gender, which is either male or female. This is a categorical feature.
- Age: The age of the user in years. This is a continuous numerical feature.
- Estimated Salary: An estimate of the user's annual salary. This is a continuous numerical feature.
- Purchased: The target variable indicating whether the user purchased the product. It is a binary feature with a value of either 0 (not purchased) or 1 (purchased).
Distribution
The data is a tabular CSV file named
User_Data.csv with a size of 10.93 kB. It contains 400 valid records across 5 columns, with no missing or mismatched data.Usage
This data is ideal for binary classification tasks. It can be used to build a model that predicts the probability of a user purchasing a product based on their age, gender, and estimated salary.
Coverage
The data covers 400 individual users. Demographically, the gender distribution is 51% female and 49% male. The age of users ranges from 18 to 60 years, with a mean age of approximately 38. The estimated annual salary ranges from £15,000 to £150,000.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building and training predictive models for customer behaviour.
- Marketing Analysts: To understand customer demographics and target potential buyers more effectively.
- E-commerce Businesses: To analyse user data for product recommendation and sales forecasting.
Dataset Name Suggestions
- Customer Purchase Prediction Data
- User Purchase Behaviour Insights
- E-commerce Customer Demographics
- Predictive Purchase Analytics
Attributes
Original Data Source: Customer Purchase Prediction Data
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