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Global Consumer Behaviour Dataset
Data Science and Analytics
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About
This dataset provides valuable insights into customer demographics, income, spending habits, and purchase behaviour. It is designed to support analysis, prediction, and customer segmentation tasks, helping to uncover the patterns that drive customer behaviour.
Columns
- name: The name of the customer, provided as textual data. There are 991 unique customer names out of 1000 records.
- age: The age of the customer, represented as numeric data. Ages range from 18 to 65, with a mean of 41.8 years and a standard deviation of 13.8.
- gender: The gender of the customer, provided as categorical data. The dataset is evenly split with 50% male and 50% female customers.
- education: The highest level of education attained by the customer, categorised. Bachelor's degrees account for 27%, PhDs for 25%, and other education levels for 48%.
- income: The annual income of the customer, in numeric format. Incomes range from £20,031 to £99,780, with a mean of £59,300 and a standard deviation of £23,200.
- country: The country of origin of the customer, as textual data. The dataset includes 239 unique countries, with Congo and Palau each representing 1% of the data, and the remaining 98% from other countries.
- purchase_frequency: The frequency of customer purchases, a numeric value ranging from 0.1 to 1.0. The mean purchase frequency is 0.55 with a standard deviation of 0.28.
- spending: The annual spending of the customer, calculated based on various factors, provided as numeric data. Spending ranges from £612 to £25,546, with a mean of £9,610 and a standard deviation of £5,480.
Distribution
The dataset is in a CSV format (customer_data.csv) and consists of 8 columns. It contains 1000 valid records across all columns, with no missing or mismatched values. The file size is 65.67 kB.
Usage
This dataset is ideal for:
- Customer analysis and understanding spending habits.
- Developing models for prediction tasks related to customer behaviour.
- Performing customer segmentation to identify distinct groups.
- Applying various data analysis techniques to uncover meaningful insights.
- Exploratory data analysis, regression, and K-Means clustering.
Coverage
The demographic scope includes customer age (18-65), gender (male/female), education level (Bachelor, PhD, Other), and annual income (£20,031-£99,780). Geographically, it covers 239 unique countries, though 98% are grouped as "Other" beyond Congo and Palau. A specific time range for the data is not indicated.
License
CC0: Public Domain
Who Can Use It
- Data Analysts: To explore customer behaviour patterns and spending trends.
- Marketing Professionals: For segmenting customers and tailoring marketing strategies.
- Business Strategists: To inform business decisions, product development, and understand market dynamics.
- Researchers: For academic studies on consumer behaviour, demographics, and economic patterns.
Dataset Name Suggestions
- Customer Demographics and Spending Habits
- Global Consumer Behaviour Dataset
- Customer Purchase and Income Analysis
- Retail Spending Pattern Data
Attributes
Original Data Source: Global Consumer Behaviour Dataset
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