Welcome to the Country Data Analysis project — a complete end-to-end exploratory data analysis (EDA) task built using Python. This project demonstrates data cleaning, feature exploration, visualization, and statistical insight extraction from real-world country-level data.
This is part of my Data Science journey where I applied core concepts of data preprocessing, Pandas, and Matplotlib/Seaborn to uncover meaningful patterns from global datasets.
| File | Description |
|---|---|
country.ipynb |
Jupyter notebook with full data analysis code and visualizations. |
Country_Data_Analysis.csv |
Primary dataset containing country-level statistics. |
data_updates.csv |
Updated/cleaned or additional version of the dataset used in some parts of the analysis. |
- Load and understand a real-world dataset
- Clean and preprocess the data
- Handle missing values, outliers, and duplicates
- Perform exploratory data analysis (EDA)
- Visualize patterns and relationships between features
- Draw insights that can support further modeling or decision-making
- Checking data types and fixing them
- Handling missing values using strategies like mean/median imputation
- Identifying and removing duplicate records
- Detecting outliers using statistical methods
- Summary statistics using Pandas
- Correlation matrix and heatmaps
- Distribution plots for numerical features
- Count plots and bar graphs for categorical features
- Histograms, boxplots, scatter plots, bar charts
- Advanced plots using Seaborn (pairplot, heatmap, etc.)
- Comparison of countries based on GDP, population, literacy rate, and more
- Creating new columns from existing ones (e.g., GDP per capita)
- Grouping and aggregating data for comparative analysis
- Top and bottom-ranked countries on various metrics
- Correlation between GDP and literacy/population/health
- Identifying trends and patterns for future predictions
| Tool | Purpose |
|---|---|
| Python 3 | Core programming |
| Jupyter Notebook | Interactive analysis |
| Pandas | Data loading, cleaning, and manipulation |
| NumPy | Numerical operations |
| Matplotlib | Basic plotting and graphs |
| Seaborn | Advanced visualizations |
| CSV | Dataset format |
- Which countries have the highest and lowest GDP?
- Is there a correlation between literacy rate and GDP?
- What countries show extreme population growth or decline?
- How do health and education metrics impact economic development?
- What outliers exist in income, health, or education features?