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SQL Data Formats Status License GitHub last commit

🌍 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.

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