Loan_Approval_Analysis
RoleData Analytics
Year2026
Project Details
The dataset was successfully cleaned by handling missing values in both numeric and categorical features. Applicant Income and Loan Amount showed a moderate positive correlation (~0.49). Credit History emerged as a crucial factor, with most applicants having a history value of 1 (good credit). Categorical analysis revealed: Majority of applicants were Male, Married, and Graduates. Most properties were located in Urban areas. Visualizations highlighted: Income distribution is highly skewed with some extreme outliers. Loan amounts are generally concentrated between 100–200 units. Gender and education slightly influence loan amounts, but Credit History is the strongest predictor.
Skills
Data AnalyticsContent AnalysisData VisualizationInsight GenerationExploratory Data Analysis
Tools
PythonPandasNumPyMatplotlibSeaborn
- This project focuses on Loan Approval Analysis using Python libraries such as
pandas,numpy,matplotlib, andseaborn. - The dataset contains 367 entries and 12 columns, including applicant details, income, loan amount, credit history, and property area.
- Initial steps involved:
- Exploratory Data Analysis (EDA) was performed using:
- Bivariate and multivariate analyses were conducted to understand how categorical and numerical features affect loan amounts and approval status.
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