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Population Data Analysis and Visualization

Project Overview

This project analyzes population trends using World Bank population datasets. The analysis focuses on India's population growth from 1960 to 2024 and compares population statistics across selected countries.

The project demonstrates data cleaning, filtering, analysis, and visualization techniques using Python.

Objectives

  • Analyze historical population growth data
  • Study India's population trend from 1960–2024
  • Compare population data across multiple countries
  • Create visualizations to identify patterns and trends

Dataset

Source: World Bank Population Dataset

Indicator: Population, total (SP.POP.TOTL)

The dataset contains population information for countries around the world from 1960 to 2024.

Technologies Used

  • Python
  • Google Colab
  • Pandas
  • NumPy
  • Matplotlib

Project Tasks

1. Data Loading

  • Imported population dataset from an online source.
  • Loaded data into a Pandas DataFrame.

2. India Population Analysis

  • Extracted population records for India.
  • Created a population trend visualization from 1960 to 2024.

3. Country Comparison

Compared population statistics of selected countries:

  • India
  • China
  • Nigeria
  • Japan
  • Canada
  • Germany
  • France

4. Data Visualization

Generated:

  • Population growth chart for India
  • Population comparison chart for selected countries
  • Multi-year comparison visualizations

Visualization 1: Population Growth of India

Visualization 2: Population Comparison of Countries

Results

The analysis shows:

  • Continuous growth in India's population over the past six decades.
  • India has one of the largest populations among the selected countries.
  • Population growth trends differ significantly between developed and developing nations.

Repository Structure

Population-Data-Analysis/
│
├── README.md
├── requirement.txt
├── project_dataset.txt (or dataset.csv)
├── task1.ipynb
└── images/
    ├── india_population.png
    └── country_comparison.png

How to Run

  1. Clone this repository
git clone <repository-link>

  1. Open the notebook in Google Colab or Jupyter Notebook.

  2. Install dependencies if required:

pip install pandas numpy matplotlib scipy
  1. Run all cells.

Author

Devki Patel

Data Science and Python Learning Project