Created by: Dionte Capleton
This project explores loan approval trends using Oracle SQL, Python (Pandas), and Power BI. It analyzes which applicant traits—such as credit history, income, gender, and location—most influence approval outcomes using a dataset of 614 applications.
To identify patterns in loan approval likelihood based on applicant attributes and financial behaviors. The project answers key business questions, such as:
- What factors influence loan approval rates?
- Which demographic groups have higher approval success?
- How do income and loan amounts vary by education, employment, and region?
- 🟨
PowerBI/- Power BI.pbixdashboard - 🧠
SQL/- SQL queries for analysis - 🐍
Python/- Data cleaning + export - 🖼️
images/- Thumbnails + dashboard slides - 📄
data/- Cleaned CSV datasets
| File | Description |
|---|---|
Loan2P.sql |
SQL queries used for analysis (joins, aggregations, insights) |
loan_project.ipynb |
Google Colab notebook for Python-based cleaning and exploration |
loan_case.csv / loan_status.csv |
Raw Kaggle data files |
loan_case_cleaned.csv / loan_status_cleaned.csv |
Cleaned CSVs used in Power BI |
loan_dashboard.pbix |
Power BI file (open in Power BI Desktop) |
loan_dashboard.pdf |
Static PDF export of final dashboard |
README.md |
Project summary and documentation |
- ✅ Applicants with strong credit histories had the highest approval rates.
- 🏙️ Semiurban applicants saw notably high loan approval.
- 👩💼 Female applicants slightly outperformed male counterparts.
- 💵 Income and property area also influenced approval outcomes.
Which applicant traits should a bank prioritize to speed up loan approvals and reduce risk?
📌 Click the image below to view full dashboard slides (PDF)
- Open
school_vis.pbixin Power BI or viewschool_vis.pdf. - Filter by gender, credit score, property area, etc.
- Use SQL queries to explore data joins and logic.
- The dataset used was sourced from Kaggle - Loan Prediction Dataset. It includes 600+ rows of loan application data across demographics, employment, and loan status fields.
Dionte Capleton
Aspiring Data Analyst | SQL, Python, Power BI