📊 Health Spending vs Life Expectancy

This project explores the relationship between how much a country spends on healthcare and its average life expectancy, using Seaborn’s built-in healthexp dataset. Through a series of visualizations, we aim to uncover patterns, outliers, and diminishing returns in healthcare investment.

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📊 Objective

• Understand how health spending relates to life expectancy

• Observe whether more spending leads to significantly longer lives

• Use visuals like log scaling and hue to reveal deeper patterns

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📁 Dataset

• Source: seaborn.load_dataset("healthexp")
• Type: Built-in Seaborn dataset
• Features
• Spending_USD: Health spending per capita
• Life_Expectancy: Average life expectancy in years
• Country: Name of the country
• Year: Year of the data (1970 to 2020)

The dataset was clean and required no preprocessing. For reproducibility, a copy is also saved as data/healthexp.csv.

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🖼️ Visualizations

1. Basic Jointplot (Scatter + Histogram)

Shows a banana-shaped curve, indicating diminishing returns of life expectancy as health spending increases.

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2. Basic Jointplot (with regression line)

Shows the overall positive relationship between spending and life expectancy. However, the shape curves like a banana, indicating diminishing returns at higher spending levels.

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3. Jointplot with Hue by Country

Adds color coding by Country to distinguish patterns per nation. Most useful when zooming into a few specific countries.

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4. Jointplot with Log Scale

Using log scale on health spending straightens the scatter plot, revealing clearer upward trends. It shows clusters of countries forming linear patterns.

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💡 Key Takeaways

• There's a positive relationship between healthcare spending and life expectancy, but the gains taper off at higher spending levels.
• The United States is a notable outlier — spending a lot more but not achieving a proportional life expectancy benefit.
• Log transformation helps better reveal meaningful clusters and relationships.
• Japan stand out with relatively high life expectancy for their spending.

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🛠️ Tech Stack

• Python
• Seaborn
• Pandas
• Matplotlib

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📂 Folder Structure

.
├── data/
│ └── healthexp.csv
├── images/
│ ├── jointplot_basic.png
│ └── jointplot_log_hue.png
├── visualize.py
├── requirements.txt
├── .gitignore
└── README.md

📘 For more detailed insight, check the notebook: HealthViz.ipynb

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🧠 Inspiration

This project was done to explore how a single, clean visualization can carry meaningful public health insight — especially for non-technical audiences. It's part of a broader interest in using data science to address health challenges in Africa and beyond.

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