HisLikeness/Supervised-Learning-Classification-using-Africa-Systemic-BankingCrisis-Dataset

# Supervised-Learning-Classification-using-Africa-Systemic-BankingCrisis-Dataset ### Overview The purpose of this project is to develop a machine learning model that predicts the likelihood of a systemic crisis emergence in African countries based on various economic indicators. The scope of this project is limited to analyzing the provided dataset, which focuses on banking, debt, financial, inflation, and systemic crises in 13 African countries from 1860 to 2014. The problem addressed by this project is the need to identify early warning signs of systemic crises in African countries, which can help policymakers and stakeholders take proactive measures to prevent or mitigate the impact of such crises. ### Key Features - Data Exploration: The exercise involves performing basic data exploration, including handling missing and corrupted values, removing duplicates, handling outliers, and encoding categorical features. - Dataset Analysis: The exercise involves analyzing a dataset related to systemic crises in African countries. - Feature Selection: The exercise involves selecting relevant features from the dataset to use in the machine learning model. - Classification Problem: The exercise involves solving a classification problem, where the target variable is a binary outcome (e.g., crisis or no crisis). - Machine Learning Model Development: The goal is to develop a machine learning model that predicts the likelihood of a systemic crisis emergence. - Model Evaluation: The performance of the machine learning model will be evaluated using relevant metrics. - Model Improvement: The exercise encourages discussing alternative ways to improve the model's performance. ### Dataset Details about the dataset: - Source: [drive.google.com] - Description: The structure of the dataset is such that it has 1059 entries, 14 Columns, a RangeIndex with Data Types of Float64 in 3 columns (exch_usd, gdp_weighted_default, inflation_ann …