Remittance Patterns and Economic Development

***The file contains code on different exploratory analysis and how it can be used to decide which to be used for forcasting.

***There are other attachment to this project like containerization with docker and accessing from a website to forecast exchange and remittance of any country in Africa

Introduction

This repository contains the research project conducted by Team Heroku, focusing on the intricate relationship between remittance patterns and economic development, with a special emphasis on African countries in the context of the COVID-19 pandemic. Our research delves into the dynamics of remittances and their impact on various economic indicators, addressing gaps left by previous studies.

Problem Statement

We aim to explore the nuanced dynamics of remittances in Africa, particularly during the COVID-19 pandemic. This involves addressing methodological gaps from previous studies by Teams Bayes and CatBoost, and providing a tailored investigation into remittance impacts on African economies.

Research Objectives

• Evaluate remittance patterns and economic development in African countries.
• Explore correlations between remittances and foreign direct investment (FDI).
• Conduct a comprehensive analysis using advanced methodologies like dynamic models and neural networks.
• Investigate the influence of various economic crises on remittance flows.

Overview of Previous Projects

Summarizing the achievements and gaps in the research conducted by Teams Bayes and CatBoost, we aim to build upon their findings, introducing more advanced and tailored methodologies for a more comprehensive understanding of remittance patterns.

Research Gaps and Opportunities

Addressing the limitations in Team CatBoost’s project, our research will focus on high-quality data analysis, advanced feature selection, and diverse modeling techniques to gain deeper insights into the remittance-economic development nexus in Africa.

Methodological Enhancements

Our approach includes:

1. Inclusion of novel datasets.
2. Event study techniques.
3. Focus on predictive feature importance.
4. Dynamic models for time-series analysis.
5. Unsupervised learning for anomaly detection.
6. Deep learning architectures for complex pattern recognition.
7. Ensemble of neural networks for robust predictions.
8. Multivariate regression analysis.

Conclusion

Team Heroku is committed to advancing the understanding of remittances and economic development in Africa. We aim to provide significant contributions through our methodological improvements and nuanced analysis.

Appendix

• GDP Data
• Remittance Paid Data
• Remittance Received Data
• Unemployment Rate Data
• Migration Data
• Population Data
• Foreign Direct Investment Data
• External Debt and Global Crises Data