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Personal Finance Awareness Metrics
Finance & Banking Analytics
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About
Investment Survey data collected from individuals regarding their knowledge of finance and the quality of their investments. The dataset establishes connections between a person’s financial awareness, their investment habits, their employment status, and their annual salary. It consists of 10 measured variables and is suitable for analysts seeking thoughtful insights into the financial line. This resource is particularly useful for practicing data visualization and label encoding.
Columns
The file contains 10 columns detailing participant information and investment choices:
- Gender: Categorical string data (e.g., Male (64%), Female (36%)).
- Age: Numeric data reflecting the participant's age, ranging from 18 to 56 years, with a mean of 25.4.
- Working_professional: Numeric flag indicating professional employment status (59% are working professionals).
- Annual_income: Numeric values detailing annual salary, spanning from 0 to £600k (using British context), with a mean of £166k.
- Mode_of_investment: String data showing investment types, with Stocks (Intraday, long term) and Mutual Funds being the most common modes.
- Investment_per_month: Numeric amounts detailing monthly investment contributions; £1000 is the most frequent amount.
- Motivation_cause: String data capturing the primary cause for investment; Family members and Friends are the top motivators.
- Resources_used: String data indicating resources relied upon for investment information, such as Family members/Friends or Mobile applications.
- Goal_for_investment: String data listing primary goals, with Wealth generation and Personal Savings highly represented.
- Duration_to_save(in_Years): Numeric data detailing the duration planned for saving, with 10 years being the most frequent response.
Distribution
The dataset is formatted for easy use, typically provided as a CSV file titled
investment_survey.csv, with a file size of approximately 11.22 kB. The structure includes 10 distinct columns and contains 100 validated records, with no missing values reported across the fields. There are currently no expected future updates for this data product.Usage
This dataset is ideal for several applications, including:
- Exploratory Data Analysis (EDA) on personal finance awareness.
- Building models to correlate income and employment with investment decisions.
- Practicing data cleaning techniques, specifically label encoding.
- Creating dynamic dashboards and reports using tools such as PowerBi desktop.
- Gaining foundational insights for entry-level financial analysis.
Coverage
The data focuses on demographic factors like Age (18–56), Gender (Male/Female), and professional status. It also includes financial attributes such as Annual Income and Investment per Month. Specific geographical or temporal scope notes are not detailed in the available materials.
License
CC0: Public Domain
Who Can Use It
- Data Science Beginners: To work with a clean, small dataset involving financial terms and structured attributes.
- Academic Researchers: Studying social determinants of financial literacy and investment choices.
- Business Analysts: Investigating how income brackets affect investment modes and motivations.
Dataset Name Suggestions
- Individual Financial Investment Survey 2024
- Salary, Employment, and Investment Quality Data
- Personal Finance Awareness Metrics
- Investment Behaviour Study (100 Records)
Attributes
Original Data Source: Personal Finance Awareness Metrics
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