# Project Title: Using RNNs to Forecast Migration Patterns

## Project Description

This project focuses on applying Recurrent Neural Networks (RNNs) to predict migration patterns based on historical migration data and socioeconomic factors. By leveraging advanced neural network architectures, students will develop models capable of forecasting migration trends and identifying potential migration hotspots. This approach can aid policymakers and researchers in understanding and managing migration flows. For similar studies, refer to:
https://arxiv.org/pdf/2005.09902
https://www.tandfonline.com/doi/pdf/10.1080/1369183X.2022.2100546?casa_token=59Y5nwCoLs8AAAAA:jYggvCM23jsJR6WPGfHeqeYV-cHaZz0YoUYqevSXCM_gQ4m7iajKGna-q7_77f9iSEVw4i4zSeLtng

## Project Steps

### Part 1: Data Collection and Preliminary Analysis (10 points)

#### Data Collection
- Obtain datasets of historical migration records and relevant socioeconomic factors such as employment rates, GDP, political stability indices, and climate data from sources like the World Bank (https://data.worldbank.org/) or UNHCR (https://www.unhcr.org/).

#### Data Cleaning
- Clean the datasets by removing duplicates, correcting errors, and filtering out irrelevant data. Implement preprocessing steps such as normalization of numerical data and encoding of categorical variables.

#### Preliminary Data Analysis and Visualization
- Conduct preliminary data analysis to understand migration patterns and their correlations with socioeconomic factors.
- Visualize the data using geographical maps for spatial distribution and time series plots for temporal trends.

### Part 2: Deep Learning Model (10 points)

#### Build RNN Model
- Develop an RNN model using PyTorch. Incorporate layers suited for sequence prediction tasks such as LSTM or GRU layers.
- Provide a detailed walkthrough of the code, which should be well-commented and available in a repository linked in the report.

#### Model Training and Validation
- Train the model on the prepared dataset, using a split of training and validation data to monitor for overfitting.
- Validate the model's performance using appropriate metrics. Adjust hyperparameters as needed to improve outcomes.

#### Deployment and Real-Time Testing
- Deploy the model to simulate real-time migration pattern forecasting and visualize the results.

### Part 3: Policy Impact Analysis (10 points)

#### Analysis Using the Predictive Model
- Analyze how the predictive model's results could influence policy decisions regarding migration management.
- Study the implications of deploying such predictive technologies, focusing on ethical considerations and potential biases.

#### Presentation and Report
- Summarize the findings and methodologies in a comprehensive report. Reflect on the model's efficacy and areas for improvement.
- Discuss potential societal impacts, including both the benefits and challenges of using AI to forecast migration patterns.

## Grading Criteria

### Presentation of Data and Preliminary Data Analysis (10 points)
- Proper handling and presentation of the dataset.
- Clarity and relevance of the visualizations.
- Thoroughness of the initial analytical findings.

### Deep Learning Model (10 points)
- Accuracy and robustness of the RNN model.
- Model architecture design and implementation.
- Detailed documentation and reproducibility of the model training process.

### Policy Impact Analysis (10 points)
- Depth and rigor of the analysis regarding the model's real-world applicability.
- Discussion of ethical considerations and potential biases.