Predicting Vehicle Ownership Dataset

Columns

• Occupation: Details the type of work an individual is employed in, covering administrative roles, skilled trades, service industry jobs, and more. This column helps in understanding employment patterns relative to other characteristics. (e.g., Chef, Electrician, with 98% validity)
• Monthly Income: Specifies the monthly earnings of each individual, useful for assessing financial stability and its correlation with credit score, employment tenure, and family size. (e.g., $4,500, $3,500, with 97% validity)
• Credit Score: A numerical representation of an individual's creditworthiness, ranging from 300 to 850, where higher scores indicate better credit. This column helps determine financial responsibility and its link to income and financial history. (Mean: 703, Std. Deviation: 69.2, with 92% validity)
• Years of Employment: Indicates the duration an individual has been employed at their current job, reflecting stability and reliability, and its relationship with income and credit score. (e.g., 4 years, 3 years, with 91% validity)
• Finance Status: Categorises an individual's financial health as stable, struggling, or in crisis, aiding in identifying financial trends based on occupation, income, and credit score. (e.g., Stable (61%), Unstable (17%), with 95% validity)
• Finance History: Describes past financial behaviour, including bill payments, loan management, and credit usage, which is key for assessing financial responsibility and patterns influenced by employment years and number of children. (e.g., No significant issues (63%), Missed payments in the past (10%), with 94% validity)
• Number of Children: Provides information on the number of children an individual has, helping to understand financial responsibilities and identify behavioural patterns related to family size. (e.g., 0, with 75% validity)
• Car Ownership: A boolean indicator specifying whether an individual owns a car (true/false). This column helps in understanding financial independence and patterns of car ownership linked to other demographic and financial factors. (e.g., true (60%), false (36%), with 96% validity)

Distribution

Usage

• Exploratory Data Analysis (EDA) to uncover trends and patterns related to car ownership.
• Building predictive models for binary classification tasks, such as predicting whether an individual owns a car.
• Educational purposes for students and practitioners learning about data science, machine learning, and financial analytics.
• Analysing relationships between socio-economic factors, financial health, and asset ownership.
• Lending institutions could use similar data for hypothetical risk assessment or model development in a controlled, non-production environment.

Coverage

• Geographic Scope: Not specified.
• Time Range: Not specified.
• Demographic Scope: Covers a variety of occupations, income levels, credit scores (ranging from 500 to 900), employment durations, and family sizes (up to 6 children recorded). Data availability varies slightly per column due to missing values (e.g., Number of Children has 25% missing values, while others like Occupation have 2%).

License

Who Can Use It

• Data Science Beginners and Intermediate Learners: For practical experience with data cleaning, EDA, and machine learning model building.
• Students and Researchers: Studying socio-economic factors, financial behaviour, and predictive analytics.
• Academics: Utilising a synthetic dataset for teaching and demonstrating concepts without privacy concerns.
• Anyone interested in understanding car ownership determinants through a data-driven approach.

Dataset Name Suggestions

• Car Ownership Prediction Dataset
• Financial & Demographic Car Ownership Data
• Predicting Vehicle Ownership Dataset
• Socio-Economic Factors and Car Ownership

Attributes