Column Descriptions:

• age (int): The age of the individual.
• gender (string): The gender of the individual, either 'Male' or 'Female'.
• occupation (string): The occupation of the individual.
• education_level (string): The highest level of education attained by the individual, such as 'High School', 'Associate's', 'Bachelor's', 'Master's', or 'Doctoral'.
• marital_status (string): The marital status of the individual, either 'Single' or 'Married'.
• income (int): The annual income of the individual in dollars.
• credit_score (int): The credit score of the individual, ranging from 300 to 850.

loan_status (string): The target variable, indicating whether the loan application was 'Approved' or 'Denied'.
This dataset contains various demographic and financial features that could be used to build a classification model for predicting loan approval or denial. The loan_status column serves as the target variable for the classification task.