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.
- 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
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.
- Python
- Google Colab
- Pandas
- NumPy
- Matplotlib
- Imported population dataset from an online source.
- Loaded data into a Pandas DataFrame.
- Extracted population records for India.
- Created a population trend visualization from 1960 to 2024.
Compared population statistics of selected countries:
- India
- China
- Nigeria
- Japan
- Canada
- Germany
- France
Generated:
- Population growth chart for India
- Population comparison chart for selected countries
- Multi-year comparison visualizations
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.
Population-Data-Analysis/
│
├── README.md
├── requirement.txt
├── project_dataset.txt (or dataset.csv)
├── task1.ipynb
└── images/
├── india_population.png
└── country_comparison.png
- Clone this repository
git clone <repository-link>-
Open the notebook in Google Colab or Jupyter Notebook.
-
Install dependencies if required:
pip install pandas numpy matplotlib scipy- Run all cells.
Devki Patel
Data Science and Python Learning Project