World Bank Data Analysis Project
World Bank Data Analysis Project is an end to end data analytics project built using public datasets from the World Bank. The project focuses on analyzing global economic indicators such as GDP and population to understand development trends, economic disparities, and country level growth patterns over time. It demonstrates data cleaning, transformation, feature engineering, statistical analysis, and visualization using Python.
Project Overview
The objective of this project is to extract, clean, and analyze real world economic datasets to generate meaningful insights. The project integrates multiple datasets including GDP, population statistics, and country level metadata. It standardizes country names using ISO codes, handles missing values using different imputation techniques, reshapes time series data for analysis, and performs exploratory data analysis to identify patterns and outliers.
Key Highlights
This project demonstrates multi source data integration, structured data cleaning, missing value handling, time series transformation from wide to long format, feature engineering for analytics readiness, statistical outlier detection using IQR method, and visualization of economic growth trends. The workflow reflects real world data engineering and analytics practices.
Workflow
The process begins with loading World Bank datasets including GDP, population, and country information. Data is cleaned by standardizing country names and converting columns into appropriate numeric and datetime formats. Missing values are treated using strategies such as mean imputation and forward fill methods. The dataset is reshaped into a time series friendly structure. Feature engineering is applied to prepare the dataset for analysis. Exploratory analysis is performed to study GDP and population trends and detect statistical outliers.
Technologies Used
Python is used as the primary programming language. Pandas and NumPy are used for data processing and transformation. Matplotlib and Seaborn are used for visualization. Pycountry and regular expressions are used for country standardization. Scikit learn can be extended for statistical modeling.
Insights Generated
The project identifies GDP growth trajectories across countries, explores the relationship between population and economic output, detects countries that are statistical outliers in economic indicators, and visualizes development trends over time.
How to Run
Clone the repository, install the dependencies listed in the requirements file, and run the Jupyter notebook to execute the complete ETL and analysis workflow.
Future Improvements
Future enhancements can include time series forecasting models for GDP prediction, building interactive dashboards using Streamlit or Plotly, integrating live data from the World Bank API, adding geospatial visualizations, and automating the ETL workflow using orchestration tools.
Author
Kartish Reddy Anugu