maxprodigy/linear_regression_model

This repository explores the application of linear regression to analyze economic trends in Africa, specifically focusing on Nigeria's GDP per capita. By leveraging data-driven insights, I aim to identify opportunities for economic growth and social development, ultimately empowering African youth through technology and innovation. # Linear Regression Model # Nigeria GDP Per Capita Predictor A machine learning project that predicts Nigeria's GDP Per Capita using Linear Regression and Random Forest models, deployed with FastAPI and a Flutter mobile application. ## Project Components 1. **Machine Learning Models** - Linear Regression Model - Random Forest Model - Data source: World Bank Nigeria Statistics (2000-2020) Link - Extracted Nigerian Dataset With Python Link - Features: Year - Target: GDP Per Capita (USD) 2. **API Endpoint** - URL: nigeria-gdp-linear-regressi… - Swagger UI Documentation Available - Accepts POST requests with: ```json { "year": 2024, "model_type": "linear" } ``` - Returns predictions in format: ```json { "year": 2024, "predicted_gdp": 3231.69 } ``` 3. **Mobile Application** - Built with Flutter - Features: - Year input (2024-2050) - Model selection (Linear/Random Forest) - Real-time predictions - Error handling and validation ## Running the Mobile App 1. Prerequisites: - Flutter SDK installed - Android Studio/VS Code with Flutter extensions - An Android/iOS emulator or physical device 2. Installation: ```bash # Clone the repository git clone github.com cd linear_regression_model.git # Install dependencies flutter pub get # Run the app flutter run ``` 3. Using the App: - Enter a year between 2024 and 2050 - Select prediction model (Linear or Random Forest) - Click "Predict" to get GDP forecast - View results in the prediction area ## Demo Video Link to YouTube Demo ## API Usage Test the API directly through Swagger UI: 1. Visit nigeria-gdp-linear-regressi… 2. Navigate to POST /predict endpoint 3. Click "Try it out" 4. Input test values: ```json { "year": 2025, "model_type": "linear" } ``` 5. Click "Execute" ## Error Handling The application handles various error cases: - Invalid year input - Network errors - Server errors - Missing values - Out …