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Exploratory Data Analysis (EDA) on World Population Dataset

📋 Project Overview

This project analyzes population trends by continent from 1970 to 2022, highlighting demographic changes over time. Using Python libraries for data handling and visualization, it fills missing values logically and visualizes results with a modern, engaging color palette. The analysis provides clear insights into population growth patterns, helping to identify trends and outliers across different continents, offering a foundation for understanding global demographic shifts.

📊 Data Description

Column Data Type Null Data Count
Rank int64 0
CCA3 object 0
Country object 0
Capital object 0
Continent object 0
2022 Population float64 4
2020 Population float64 1
2015 Population float64 4
2010 Population float64 7
1990 Population float64 5
1980 Population float64 5
1970 Population float64 4
Area (km²) float64 2
Density (per km²) float64 4
Growth Rate float64 2
World Population Percentage float64 0

📑 Dataset

The dataset that i use for this analysis World Population Dataset.

🛠 Methodology

Data Preprocessing

  • Handled missing values using linear interpolation, ensuring a smooth filling of gaps in the data.

Statistical Analysis

  • Performed basic statistical analysis with methods like info() and describe() to understand the dataset.
  • Calculated the correlation matrix to identify relationships between variables.

Data Visualization

  • Created univariate, bivariate, and multivariate visualizations using Matplotlib and Plotly, enabling high-resolution, interactive insights into population trends.

Reporting & Documentation

  • Saved visualizations in HTML format for easy, interactive viewing.
  • Compiled comprehensive reporting on significant findings, including key population trends and correlations.

🔍 Key Insights

Linear Interpolation was used to handle missing data, providing a more accurate representation of the population over time. Identified significant correlations between population changes and time periods, visualized through both static and interactive charts. The visualization of population trends across continents helped in understanding demographic growth patterns.

Contributions

We welcome any feedback and contributions. If you would like to contribute to the project, please submit a pull request or open an issue.