This project showcases end-to-end data analysis combining Microsoft Excel and Python to extract actionable insights. Each dataset was explored, cleaned, and visualized using professional-grade tools. With experience in data analytics, this work is a reflection of practical, high-impact analytics practices.
Includes:
- EMPLOYEE DATASET: Salary and experience breakdown across departments and roles
- AD BUDGET VS SALES: ROI and budget efficiency for companies
- LOANS AND INTEREST: Interest computation and repayment analysis
- PRICE DISCOUNT: Discount modeling and revenue impact
A Python notebook used to:
- Clean data
- Perform ROI and repayment computations
- Visualize trends using Seaborn, Matplotlib, and Plotly
- Average Salary by Role (
avg_salary_by_role.png) - Experience vs Salary (Interactive)(
thumbnails/exp_vs_salary_thumb.png)](https://oreoluwaanjorin.github.io/excel-python-portfolio/visuals/exp_vs_salary.html) - Top ROI Companies (
top_roi_companies.png) - Budget vs Sales with Regression (
thumbnails/budget_vs_sales_thumb.png)](https://oreoluwaanjorin.github.io/excel-python-portfolio/visuals/budget_vs_sales.html) - Interest Rate Distribution (
interest_rate_dist.png) - Repayment by Term (Box Plot) (
thumbnails/repayment_by_term_thumb.png)](https://oreoluwaanjorin.github.io/excel-python-portfolio/visuals/repayment_by_term.html) - Revenue Loss from Discounts (
discount_impact.png)
| Tool/Library | Purpose |
|---|---|
| Microsoft Excel | Initial data capture, raw data structure |
| pandas | Data manipulation, transformation |
| matplotlib.pyplot | Static plotting (bar, histogram) |
| seaborn | Enhanced statistical visualizations |
| numpy | Numerical calculations and estimators |
| plotly.express | Interactive, browser-based visualization |
Oreoluwa Anjorin
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