Cancer-cost-analytics

Data analysis project using Excel, Power BI & Tableau to visualize cancer risk and treatment cost.

1. Cancer Risk & Cost Analytics Dashboard

This project analyzes a large-scale cancer dataset of 50,000 patients using Excel, Power BI, and Tableau. The objective was to uncover insights related to treatment costs, survival outcomes, risk levels, and demographic disparities across multiple regions and cancer types.

2. Project Overview

• Cleaned and enriched the dataset with custom columns for Risk Level, Treatment Cost Tier, and Survival Category using Excel formulas (IF, AVERAGEIF, etc.).
• Created interactive dashboards in Power BI and Tableau to visualize KPIs across gender, cancer stage, region, and cost brackets.
• Used DAX queries to derive age groups, survival percentages, death ratios, and high-risk flags based on environmental and genetic factors.
• Applied statistical analysis (including correlation) to understand the relationship between risk factors (like smoking, air pollution) and cancer severity.

3. Tools & Technologies Used

• Microsoft Excel – Pivot tables, custom formulas, risk modeling
• Power BI – DAX queries, KPI dashboards, slicers, filters
• Tableau – Visual analytics (bar charts, scatter plots, pie charts)

4. Key Insights

• Stage IV cancers- contributed to the highest total cost burden (~$1.52B), while Stage 0 showed the highest average survival.
• Lung and Breast cancer were the most economically impactful cancer types.
• Patients classified as "High Risk" (Genetic Risk, Smoking, etc.) had lower survival rates and higher treatment costs.
• Gender disparities and regional economic differences were clearly visible in cost and survival distribution.

5. Sample Visuals

Tableau Dashboard	Power BI Dashboard

6. Downloadable Dashboard Files

• Power BI (.pbix)
• Tableau (.twbx)