lukehoward46/predict-migration

Predict Migration with Machine Learning

Overview

This project focuses on predicting human migration between countries using Machine Learning. Human migration is a crucial factor in city planning, international trade, the spread of infectious diseases, conservation planning, and public policymaking. By leveraging machine learning techniques, this project aims to provide accurate migration predictions that can assist in these critical areas.

Project Description

The prediction of human migration involves analyzing historical migration data and utilizing machine learning models to forecast future migration trends. This project demonstrates how to preprocess the data, handle categorical variables, deal with missing values, and build a predictive model using machine learning algorithms.

Key Steps:

1. Data Preparation:

   • Data Loading: The project begins by loading a dataset containing migration data, including measures like arrivals, departures, and net migration, along with country and citizenship information.
   • Data Encoding: Categorical variables such as "Measure," "Country," and "Citizenship" are converted into numerical values to be used in the machine learning model.
   • Handling Missing Values: Any missing values in the dataset are identified and filled using appropriate statistical methods, such as the median value.

2. Data Splitting:

   • The dataset is split into training and testing sets, with 70% of the data used for training the model and 30% reserved for testing. This ensures that the model's performance can be evaluated on unseen data.

3. Model Building:

   • A machine learning model, specifically a Random Forest Regressor, is trained on the prepared data. The model is chosen for its ability to handle complex datasets and its robustness in providing accurate predictions.
   • The model's performance is evaluated using the R² score, which indicates how well the model's predictions match the actual migration data.

4. Data Visualization:

   • The project includes visualizations to analyze and understand migration trends over time. Line and bar plots are used to depict the growth of migration, while correlation heatmaps provide insights into the relationships between different variables in the dataset.

Project Goals:

• Understand: Gain insights into how machine learning can be applied to predict human migration between countries.
• Implement: Learn how to preprocess migration data, encode categorical variables, and handle missing values.
• Evaluate: Assess the accuracy and effectiveness of a Random Forest model in predicting migration trends.

Conclusion

This project provides a practical approach to predicting human migration using machine learning. By preparing the data appropriately and using robust machine learning models, it is possible to generate accurate predictions that can inform various aspects of public policy, urban planning, and more.

Future Work

• Model Improvement: Experiment with different machine learning algorithms such as Support Vector Machines or Neural Networks to improve prediction accuracy.
• Feature Engineering: Incorporate additional features such as economic indicators, population density, or political stability to enhance the model's predictive power.
• Real-Time Prediction: Extend the project to include real-time data for up-to-date migration predictions, potentially integrating data from live sources like immigration agencies or border control systems.

Contributions

Contributions are welcome! If you have suggestions or improvements, feel free to fork this repository, implement your ideas, and submit a pull request.

License

This project is open-source and available under the MIT License. Feel free to use, modify, and distribute the project as needed.