BIGTUNZ/Systemic-Crisis-Prediction-in-African-Countries

Welcome to my GitHub repository, where I showcase a variety of data science projects that highlight my skills and expertise in data analysis, machine learning, and visualization. Each project demonstrates my ability to apply data-driven techniques to solve real-world problems across different domains. # Systemic Crisis Prediction in African Countries ## Overview The "Systemic Crisis Prediction in African Countries" project aims to predict the likelihood of systemic crises (including banking, financial, and inflation crises) in selected African countries using historical economic data from 1860 to 2014. By analyzing key indicators such as annual inflation rates and exchange rates, this project provides valuable insights for policymakers and stakeholders interested in economic stability. ## Table of Contents - Installation - Dataset - Exploratory Data Analysis - Modeling - Evaluation Metrics - Usage - Contributing - License - Acknowledgments ## Installation To run this project, clone the repository and install the required dependencies: ```bash git clone github.com cd systemic-crisis-prediction pip install -r requirements.txt ``` ## Dataset The dataset used in this project is sourced from Kaggle and contains various economic indicators for 13 African countries, including Algeria, Angola, and Nigeria. Key columns include: country_number country_code country year systemic_crisis exch_usd domestic_debt_in_default sovereign_external_debt_default gdp_weighted_default inflation_annual_cpi independence currency_crises inflation_crises banking_crisis ## Exploratory Data Analysis During the EDA phase, the following tasks were performed: Summary statistics were generated to understand the dataset's distribution. Inflation trends and exchange rates were visualized using libraries such as Matplotlib and Seaborn. ## Modeling This project employs a machine learning model to predict the likelihood of systemic crises. The algorithm implemented was: Decision Tree Classifier ## Evaluation Metrics Model performance was assessed using the following metrics: Accuracy: The proportion of correctly predicted instances out of the total instances, indicating the overall effectiveness of the model. Confusion Matrix: A table used t …