World Population Analysis

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Project Overview

The purpose of this project is to study global population trends using historical data and forecast future population growth. This involves applying machine learning techniques to analyze demographic data, identify factors influencing population changes, and develop predictive models.

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Dataset Overview

Source

Dataset retrieved from World Population Review. For direct access, you can download it via this link.

Glossary (Column-Wise Description)

• Rank: Rank of the country/territory by population size.
• CCA3: Three-digit country/territories code.
• Country/Territories: Official name of the country/territory.
• Capital: Name of the capital city.
• Continent: Continent where the country/territory is located.
• 2022 Population: Population of the country/territory in 2022.
• 2020 Population: Population of the country/territory in 2020.
• 2015 Population: Population of the country/territory in 2015.
• 2010 Population: Population of the country/territory in 2010.
• 2000 Population: Population of the country/territory in 2000.
• 1990 Population: Population of the country/territory in 1990.
• 1980 Population: Population of the country/territory in 1980.
• 1970 Population: Population of the country/territory in 1970.
• Area (km²): Total area of the country/territory in square kilometers.
• Density (per km²): Population density per square kilometer.
• Growth Rate: Annual population growth rate.
• World Population Percentage: Percentage of the world population represented by each country/territory.

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Acknowledgements

This dataset was sourced from World Population Review.
Cover photo credit: Vector created by rawpixel.com - www.freepik.com.

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Tools Used

• Programming Language: Python
• Platform: Jupyter Notebook

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Workflow Steps

1. Data Collection

The dataset was obtained and verified for consistency and quality.

2. Data Preprocessing

Cleaning and formatting the dataset to handle missing values and ensure compatibility with machine learning models.

3. Exploratory Data Analysis (EDA)

Conducting initial analyses to understand data trends and distributions.

4. Feature Engineering

Extracting and selecting features that contribute most significantly to population growth prediction.

5. Model Building

Utilizing machine learning algorithms to create predictive models.

6. Model Evaluation

Assessing the performance of models using metrics like accuracy, R-squared values, and root mean square error.

7. Visualization

Creating graphical representations to display trends, distributions, and insights effectively.

8. Report Generation

Compiling findings and results into a comprehensive report.

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Feel free to suggest changes or ask for additional sections!