🌍 World Population Analysis

An end-to-end data analytics project exploring global population trends using Python (Jupyter Notebook) and Power BI. This project demonstrates skills in data cleaning, exploratory data analysis (EDA), data visualization, and dashboard design.

🧠 Project Objective

Analyze population data to uncover key insights about demographic distribution, growth patterns, and top contributing countries.

Use Python for detailed EDA, cleaning, and visual exploration.

Build an interactive Power BI dashboard for dynamic reporting and stakeholder presentation.

Installation

▶️ Run Jupyter Notebook:

# Optional: Create virtual environment
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

# Install required packages
pip install -r requirements.txt

# Launch notebook
jupyter notebook World_Population_EDA.ipynb

📥 View Power BI Report:

1. Install Power BI Desktop (if not already installed): Download Power BI

2. Open the Report:

    Launch Power BI Desktop.

    Open the World_Population_Analysis_Report.pbix file.

3. Explore the Dashboard:

    Use filters and slicers to navigate population trends.

    Analyze charts to derive insights for research or business use.

Tech Stack

Python (Jupyter Notebook): pandas, matplotlib, seaborn, plotly

Power BI: Data modeling, DAX, interactive visualizations

Excel/CSV: Raw data handling

GitHub: Version control and documentation

Files in This Repository

1. world_population.csv	:   Cleaned dataset containing country-wise population data. 
2. World_Population_EDA.ipynb : 	Jupyter Notebook performing data cleaning and analysis.
3. World_Population_Analysis_Report.pbix	: Power BI dashboard for interactive visualizations.
4. Images	: Image files used for previewing visuals in the README.

📓 Exploratory Data Analysis (Jupyter Notebook)

Performed deep exploratory data analysis in World_Population_EDA.ipynb:

🔍 Key EDA Steps:
        Data cleaning & type conversion

        Continent-wise analysis

        Top N countries by population

        Time series population growth trends

        Outlier detection using boxplots

        Correlation and summary stats

🖼 Sample EDA Visuals: Continent-wise Population Distribution

📈 Population Growth Over Time

🏳️ Top 10 Most Populated Countries

📊 Power BI Dashboard

The World_Population_Analysis_Report.pbix file presents the same data in an interactive format using Power BI.

🔹 Features:
    Dynamic continent filters

    Country-level drill-down

    Year-over-year growth comparison

    Interactive cards, bar charts, and line graphs

🖼 Dashboard Preview 📌

📈 Future Enhancements

1. Add forecasting (ARIMA or Prophet) for future population growth

2. Integrate GDP, urbanization, or fertility rate metrics

3. Deploy dashboard to Power BI Service for online access

4. Build Streamlit or Flask web version for Python-based UI

Authors

• AMRUTHA C

.NET Developer | Python Data Analyst | Power BI Enthusiast