bank_survey_formatted.csv

<br>**1. Bank Survey Formatted Dataset (bank_survey_formatted.csv)**This dataset presents the survey responses in a human-readable, formatted version with categorical variables properly labeled. It contains 580 observations with the following variables:<br>- ID: Unique identifier for each respondent (Integer: 1-580)- Age: Respondent's age in years (Range: 18-65)- Education: Educational attainment categorized into five levels (High School, Diploma, Bachelor's Degree, Master's Degree, Doctorate)- Income: Annual income in USD, formatted with dollar signs and commas (e.g., "$45,000")- ValueAddedServices: Perception of bank's additional services (5-point Likert: Strongly Disagree to Strongly Agree)- Reputation: Assessment of bank's reputation (5-point Likert: Strongly Disagree to Strongly Agree)- PerceivedCosts: Evaluation of banking costs (5-point Likert: Very Low to Very High)- PerceivedRisk: Assessment of banking risks (5-point Likert: Very Low to Very High)- PreferredBank: Customer's bank choice (Categorical: Public Bank or Private Bank)<br>This version is ideal for:- Data visualization- Report generation- Stakeholder presentations- Descriptive statistics<br>**2. Bank Survey Numeric Dataset (bank_survey_numeric.csv)**This dataset contains the same information but in numeric format, optimized for statistical analysis. It includes 580 observations with these variables:<br>- ID: Unique identifier (Integer: 1-580)- Age: Numeric age (Integer: 18-65)- Education: Numeric education level (1=High School, 2=Diploma, 3=Bachelor's, 4=Master's, 5=Doctorate)- Income: Raw income values without formatting (Integer)- ValueAddedServices: Numeric Likert responses (1-5)- Reputation: Numeric Likert responses (1-5)- PerceivedCosts: Numeric Likert responses (1-5)- PerceivedRisk: Numeric Likert responses (1-5)- PreferredBank: Binary outcome (0=Public Bank, 1=Private Bank)<br>This version is optimal for:- Statistical modeling- Binary logistic regression- Correlation analysis- Machine learning applicationsBoth datasets maintain consistent information but serve different purposes in the analysis pipeline. The formatted version enhances readability and presentation, while the numeric version facilitates statistical computations and modeling. Together, they provide a complete framework for analyzing customer bank preferences while maintaining data integrity and analytical flexibility.