OsatoOsazuwa/Systemic-Crisis-ML-Prediction-in-Africa

This project analyzes a dataset (1860-2014) from 13 African countries to predict systemic crisis emergence using indicators like annual inflation rates. # Systemic Crisis Prediction in Africa ## Table of Contents 1. Project Summary 2. Dataset 3. Machine Learning Algorithms Used 4. Methodology 5. Project Files 6. How to Run 7. Results 8. Future Work ## Project Summary This project predicts systemic crises in 13 African countries (1860-2014) using a dataset of financial indicators like annual inflation rates. The aim is to develop a classification model to assess the likelihood of systemic crisis emergence. ## Dataset Dataset can be found here ## Machine Learning Algorithms Used The following algorithms were implemented and compared: - Logistic Regression - Random Forest Classifier - Decision Tree Classifier - Support Vector Machines (SVM) - XGBoost Classifier - KNN ## Methodology - **Data Preprocessing**: - Addressed missing values and outliers. - Encoded categorical variables into numerical formats. - **Model Training and Optimization**: - Split dataset into training (80%) and test (20%) sets. - Used cross-validation for performance evaluation. - Hyperparameter tuning with RandomisedSearchCV to optimize model performance. - **Evaluation**: - Analyzed model predictions using confusion matrix. - Accuracy, precision, recall and F1-score metrics were computed also. ## Project Files Systemic_Crisis_ML_Prediction.ipynb: Contains the full implementation, including data preprocessing, model training, evaluation, and results. ## How to Run 1. Clone the repository: ```bash git clone ``` 2. Install required libraries: ```bash pip install -r requirements.txt ``` 3. Open and run the Jupyter notebook: ```bash jupyter notebook Systemic_Crisis_ML_Prediction.ipynb ``` ## Results The project successfully identifies patterns in financial indicators that predict systemic crises. Models are evaluated and compared based on performance metrics, with recommendations for improvements. ## Future Work - Incorporate additional financial and macroeconomic data for better insights. - Experiment with advanced ensemble models (e. …