地球资源数据云——数据资源详情
贷款审批数据集
该数据集《Loan Approval Dataset》主要用于二分类任务，数据形态以表格为主，应用场景偏向金融风控。 题目说明：Predict loan eligibility based on applicant demographics, financial history 任务类型：表格二分类。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：loanapproval.csv。 This dataset contains financial and demographic information on 1,000 loan applicants, designed to facilitate the development of machine learning models for credit risk assessment and loan approval prediction. It includes key features such as annual income, credit score, loan amount, number of dependents, and employment status. The target variable, loan_approved, indicates whether a loan was granted (1) or denied (0). This dataset is ideal for practicing binary classification, data visualization, and exploratory data analysis (EDA) within the banking and finance domain.
摘要概览
该数据集《Loan Approval Dataset》主要用于二分类任务，数据形态以表格为主，应用场景偏向金融风控。 题目说明：Predict loan eligibility based on applicant demographics, financial history
任务类型：表格二分类。
建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：loanapproval.csv。
This dataset contains financial and demographic information on 1,000 loan applicants, designed to facilitate the development of machine learning models for credit risk assessment and loan approval prediction. It includes key features such as annual income, credit score, loan amount, number of dependents, and employment status.
The target variable, loan_approved, indicates whether a loan was granted (1) or denied (0). This dataset is ideal for practicing binary classification, data visualization, and exploratory data analysis (EDA) within the banking and finance domain.