🌍 World Life Expectancy Analysis with SQL

This project explores global life expectancy trends using SQL. It is based on the Analyst Builder: MySQL for Data Analytics course and highlights data cleaning, exploration, and analysis using real-world indicators.

📦 Dataset

• CSV File: WorldLifeExpectancy.csv
• JSON File: WorldLifeExpectancy.json
• The dataset includes life expectancy, GDP, development status, BMI, and adult mortality across multiple countries over several years.

🧹 Data Preparation

The cleaning and transformation steps were completed using SQL and include:

• Removing duplicates with CONCAT(Country, Year)
• Handling missing values in Status, Life expectancy, and BMI
• Filtering anomalies such as zero or negative values
• Ensuring data types and constraints for analysis

🧠 Analysis

Key queries were performed in the script:

• World_Life_Expectacy_Script.sql

Insights include:

• Trends in life expectancy by country and development status
• Correlations with GDP, BMI, and adult mortality
• Outlier detection and year-over-year improvements

📤 Output

• Cleaned and analyzed results exported as:
  World_life_expectancy_output.csv

This file includes top countries by life expectancy, yearly trends, and categorical summaries.

📁 Repository Structure

├── data/ │ ├── WorldLifeExpectancy.csv │ └── WorldLifeExpectancy.json
├── scripts/ │ └── World_Life_Expectacy_Script.sql ├── output/ │ └── World_life_expectancy_output.csv └── README.md

🛠️ Tools Used

• MySQL / SQL Workbench
• Git & GitHub
• Data from Analyst Builder course

📌 Key Takeaways

• Life expectancy rises with economic development and healthcare investment.
• Developing countries show greater volatility in trends.
• Adult mortality and BMI are strong life expectancy indicators.

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Project created for educational and portfolio purposes.
Feel free to fork or star ⭐ the repo if you find it useful!