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https://github.com/adilshamim8/study_abroad

A comprehensive dataset and interactive notebook detailing tuition, living, visa, and insurance costs for global study programs—across countries, cities, and universities—to help prospective students compare and plan their abroad expenses.
https://github.com/adilshamim8/study_abroad

data-analysis data-visualization eda education educational study study-guide study-project studying

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A comprehensive dataset and interactive notebook detailing tuition, living, visa, and insurance costs for global study programs—across countries, cities, and universities—to help prospective students compare and plan their abroad expenses.

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README 

          
# Study Abroad Cost Explorer 🎓



A comprehensive analysis toolkit and interactive web application for exploring the **Cost of International Education** across major study-abroad destinations worldwide.



> streamlit website: https://study-abroad.streamlit.app/

> 

##  Features



###  Interactive Streamlit Web Application

- **Data Exploration Dashboard**: Interactive filters, visualizations, and statistics

- **ML-Powered Cost Prediction**: AI model predicting annual study costs

- **User-Friendly Interface**: Intuitive controls with preset buttons and helpful tooltips

- **Real-time Analysis**: Dynamic charts and cost breakdowns



###  Jupyter Notebooks

- **Cost Analysis Notebook**: Comprehensive EDA with visualizations

- **Model Training Notebook**: Machine learning pipeline for cost prediction



###  Dataset

- **International_Education_Costs.csv**: Curated data on tuition, living expenses, visa fees, and more

- **50+ countries** with detailed cost breakdowns



---



##  Quick Start



### Option 1: Run the Streamlit Web App (Recommended)



1. **Clone the repository**

   ```powershell

   git clone https://github.com/AdilShamim8/Study_Abroad.git

   cd Study_Abroad-main

   ```



2. **Create virtual environment**

   ```powershell

   python -m venv venv

   .\venv\Scripts\Activate.ps1

   ```



3. **Install dependencies**

   ```powershell

   pip install -r requirements.txt

   ```



4. **Run the app**

   ```powershell

   streamlit run app.py

   ```



5. **Open in browser**

   - Local: http://localhost:8501

   - Network: http://192.168.0.101:8501



### Option 2: Run Jupyter Notebooks



```powershell

pip install jupyter pandas matplotlib seaborn

jupyter notebook

```



Then open:

- `Cost-of-studying-abroad.ipynb` for exploratory analysis

- `train_model.ipynb` for model training details



---



##  Dataset Overview



The dataset (`International_Education_Costs.csv`) includes:



| Column | Description |

|--------|-------------|

| **Country** | Destination country |

| **City** | Specific city location |

| **University** | Institution name |

| **Program** | Academic program/major |

| **Level** | Degree level (Bachelor's, Master's, PhD) |

| **Duration_Years** | Program length in years |

| **Tuition_USD** | Annual tuition fee in USD |

| **Living_Cost_Index** | Cost of living score (100 = baseline) |

| **Rent_USD** | Average monthly rent in USD |

| **Visa_Fee_USD** | Student visa application fee |

| **Insurance_USD** | Annual health insurance cost |

| **Exchange_Rate** | Local currency to USD rate |



**Target Variable**: `Estimated_Annual_Cost` = Tuition + Living Costs + Rent×12 + Visa + Insurance



---



##  Web App Features



### 1. Overview Section

- Quick statistics: countries, universities, programs

- Data quality metrics

- Missing values analysis



### 2. Data Exploration

- **Interactive Filters**: Country, Level, Program, Duration

- **Visualizations**:

  - Tuition and living cost distributions

  - Country-wise cost comparisons (Top 25)

  - Living cost vs. rent scatter plot with trendline

  - Global choropleth map of average costs

- **Statistics**: Descriptive stats for filtered data



### 3. Model Demonstration

- **User-Friendly Inputs**:

  -  Living Cost presets (Low/Medium/High)

  -  Currency quick-select buttons (USD, EUR, GBP, CAD, AUD, INR)

  - Visual indicators for cost levels

  - Helpful tooltips and examples

- **Predictions**:

  - AI-powered annual cost estimation

  - Cost breakdown with monthly estimates

  - Model performance metrics (MAE, R²)

- **Real-time Validation**: Input error checking



### 4. About Section

- Dataset documentation

- Model architecture details

- Feature explanations



---



##  Machine Learning Model



### Model Details

- **Algorithm**: Random Forest Regressor (best performer)

- **Preprocessing**: 

  - One-Hot Encoding for categorical features

  - Standard Scaling for numerical features

- **Features Used**: Country, Level, Program, Duration_Years, Living_Cost_Index, Exchange_Rate

- **Performance**: High R² score with low MAE



### Model Pipeline

The trained model (`model.pkl`) includes:

1. Preprocessing transformers (ColumnTransformer)

2. Trained Random Forest Regressor

3. Full end-to-end prediction pipeline



---



##  Technology Stack



### Web Application

- **Streamlit**: Interactive web framework

- **Plotly**: Interactive visualizations

- **Pandas & NumPy**: Data manipulation

- **scikit-learn**: Machine learning



### Analysis & Training

- **Jupyter**: Interactive notebooks

- **Matplotlib & Seaborn**: Static visualizations

- **scipy & statsmodels**: Statistical analysis



---



##  Project Structure



```

Study_Abroad-main/

├── app.py                              # Main Streamlit application

├── International_Education_Costs.csv   # Dataset

├── model.pkl                           # Trained ML model

├── requirements.txt                    # Python dependencies

├── train_model.ipynb                   # Model training notebook

├── Cost-of-studying-abroad.ipynb      # EDA notebook

├── utils/

│   └── data_model.py                  # Helper functions

├── tests/

│   └── test_app.py                    # Unit tests

├── venv/                              # Virtual environment

└── README.md                          # Documentation

```



---



##  Testing



Run tests to verify functionality:



```powershell

.\venv\Scripts\Activate.ps1

pip install pytest

pytest -q

```



Tests cover:

- Data loading and cleaning

- Model loading and prediction

- Metrics computation



---



##  Usage Examples



### Example 1: USA Master's Program

- **Country**: USA

- **Level**: Master

- **Program**: Computer Science

- **Duration**: 2 years

- **Living Cost**: 100 (Moderate)

- **Exchange Rate**: 1.0 (USD)



### Example 2: UK Graduate Program

- **Country**: United Kingdom

- **Level**: Master

- **Program**: Business Administration

- **Duration**: 1 year

- **Living Cost**: 140 (Expensive - London)

- **Exchange Rate**: 0.79 (GBP)



### Example 3: Germany Undergraduate

- **Country**: Germany

- **Level**: Bachelor

- **Program**: Engineering

- **Duration**: 3 years

- **Living Cost**: 75 (Affordable - Berlin)

- **Exchange Rate**: 0.92 (EUR)



---



##  Troubleshooting



### scikit-learn Version Mismatch

If you see version warnings:

```powershell

pip install --upgrade scikit-learn==1.6.1

```



### Port Already in Use

If port 8501 is busy:

```powershell

streamlit run app.py --server.port 8502

```



### Module Import Errors

Ensure virtual environment is activated:

```powershell

.\venv\Scripts\Activate.ps1

pip install -r requirements.txt

```



---



##  Key Insights from Analysis



### Cost Patterns

- **Tuition vs. Living Costs**: Inverse relationship in some regions

- **Regional Variations**: Europe offers lower tuition, USA has higher costs

- **Program Impact**: STEM programs generally cost more than humanities



### Budget Planning

- **Low Budget**: Germany, France, Norway (€800-1,200/month)

- **Medium Budget**: Canada, Australia, Netherlands (€1,200-1,800/month)

- **High Budget**: USA, UK, Switzerland (€1,800-3,000/month)



### Hidden Costs

- Visa fees range from $50-500

- Health insurance: $500-3,000/year

- Exchange rate fluctuations can impact budgets by 10-20%



---



##  Contributing



Contributions are welcome! Here's how:



1. Fork the repository

2. Create a feature branch (`git checkout -b feature/AmazingFeature`)

3. Commit changes (`git commit -m 'Add AmazingFeature'`)

4. Push to branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request



### Ideas for Contribution

- Add more countries/universities

- Improve ML model accuracy

- Add scholarship data integration

- Implement cost comparison tools

- Add currency conversion API



---



##  Acknowledgments



- UNESCO & OECD for education statistics

- Numbeo & Mercer for cost of living data

- scikit-learn & Streamlit communities

- All contributors and users



---



## License



Licensed under the [License](License) file in the repository.



---



**⭐ Star this repo if you find it helpful!**



Last Updated: November 2025



---



Developed by [Adil Shamim](https://adilshamim.me/)



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