Skip to content

Code Academy Data Science Path Portfolio Project Life Expectancy and GDP of Data Visualization

Notifications You must be signed in to change notification settings

shahira-sadat/Portfolio-Project-Life-Expectancy-and-GDP

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 

Repository files navigation

Portfolio-Project-Life-Expectancy-and-GDP

Code Academy Data Science Path Portfolio Project Life Expectancy and GDP of Data Visualization

Project Overview

This project investigates the relationship between the economic output of a country (GDP) and the life expectancy of its citizens. Using data from the World Health Organization and the World Bank, we will analyze, prepare, and plot data to identify trends and correlations.

Project Objectives

  1. Import and preprocess the dataset.
  2. Perform exploratory data analysis (EDA) and visualize data using Seaborn and Matplotlib.
  3. Analyze the relationship between GDP and life expectancy.
  4. Summarize findings and share insights in a blog post.

Prerequisites

Ensure that you have a solid understanding of the following topic:

  • Python Fundamentals
  • Data Acquisition
  • Data Visualization
  • Hypothesis Testing
  • Summarizing Quantitative Data
  • Data Wrangling and Tidying
  • Data Manipulation with Pandas

Files

The repository includes the following files:

  • life_expectancy_gdp.ipynb: Jupyter Notebook with analysis description, code, and visualizations.
  • all_data.csv: CSV file containing GDP and life expectancy data used in the analysis.

Getting Started

  1. Clone the repository:

    git clone git@github.com:shahira-sadat/Portfolio-Project-Life-Expectancy-and-GDP.git
    
  2. Navigate to the project directory:

    cd Portfolio-Project-GDP-and-Life-Expectancy
    
  3. Open the Jupyter Notebook:

    jupyter notebook
    
  4. Start exploring the OKCupid_Data_Analysis.ipynb notebook:

    life_expectancy_gdp.ipynb

Overview

The script does the following:

  1. Loading Data:

    • Imports the CSV file into a DataFrame and inspects the data.
  2. Exploratory Data Analysis (EDA):

    • Cleans and preprocesses the data.
    • Visualizes the distribution of GDP and life expectancy.
    • Calculates and visualizes average GDP and life expectancy by country.
  3. Analysis:

    • Analyzes trends over time and the correlation between GDP and life expectancy.
  4. Summary and Insights:

    • Summarizes findings and provides insights on the relationship between GDP and life expectancy.

Feel free to modify and extend the script according to your needs.

Author

👤 Shahira Sadat

Contributions, issues, and feature requests are welcome!

Show your support

Give a ⭐️ if you like this project!

About

Code Academy Data Science Path Portfolio Project Life Expectancy and GDP of Data Visualization

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published