This project aims to predict banking crises in African countries using machine learning techniques. It utilizes a dataset containing various financial and economic indicators to build a predictive model. The model is developed using Python and TensorFlow, with data preprocessing and visualization done with libraries such as NumPy, pandas, and Plotly Express.
Banking crises can have a significant impact on the economy and the livelihoods of people in affected regions. This project explores the use of machine learning to predict the likelihood of banking crises in African countries based on historical data. By analyzing various financial and economic indicators, we aim to create a predictive model that can assist in early crisis detection.
We use a dataset that contains relevant features and information about African countries. The dataset includes economic and financial variables, as well as labels indicating whether a banking crisis occurred. The data is preprocessed to prepare it for training and evaluation.
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Start by running the project, which includes data preprocessing, model training, and evaluation.
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Explore the Jupyter Notebook or Python script to understand the steps taken to predict banking crises.
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Modify the model architecture, hyperparameters, or feature engineering techniques as needed for further experimentation.
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Feel free to visualize the results and metrics using the provided code and Plotly Express for interactive data visualization.
Contributions to this project are welcome. If you would like to contribute, please follow these steps:
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Fork the repository.
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Open a pull request to the main repository with a detailed description of your changes.
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code as per the license terms.