AravindInish/African-Banking-Econimic-Crisis-

# 🌍 Africa Banking Crisis Prediction ### 🧠 Deep Learning × Economic Intelligence Predicting banking-crisis conditions from historical African economic indicators using Deep Learning. Overview • Architecture • Model • Evaluation • Deployment --- ## 🌍 From Economic Data to Crisis Intelligence Financial crises rarely emerge from a single indicator. They are shaped by a complex interaction of **inflation, currency instability, sovereign debt, domestic debt, systemic stress and broader economic conditions**. This project explores whether those historical patterns can be learned by a **Deep Neural Network** and transformed into an interactive prediction system. > **Can historical economic signals help us identify banking-crisis conditions?** This project attempts to answer that question using a complete **Data → Deep Learning → Evaluation → Deployment** pipeline. --- ## ⚡ What This Project Does ```text 🌍 ECONOMIC DATA │ ▼ 📊 DATA ANALYSIS │ ▼ 🧹 PREPROCESSING │ ┌──────┴──────┐ ▼ ▼ Numerical Categorical Scaling Encoding │ │ └──────┬──────┘ ▼ 🧠 DEEP NEURAL NETWORK │ ▼ 📈 MODEL TRAINING │ ▼ 🎯 EVALUATION │ ▼ 💾 SAVED MODEL │ ▼ 🌐 STREAMLIT APP │ ▼ 🔮 CRISIS PREDICTION ``` --- ## 🧠 The Core Idea The system takes historical economic and financial indicators as input and learns relationships between those indicators and the `banking_crisis` target. ### Input ```text 🏦 Banking Indicators 💰 Debt Indicators 📉 Inflation Indicators 💱 Currency Indicators 🌍 Country Information 📅 Historical Information ⚠️ Crisis Indicators ``` ⬇️ ### Deep Learning Model ```text Input Features ↓ Dense Layer — 128 Neurons ↓ ReLU Activation ↓ Dropout — 30% ↓ Dense Layer — 64 Neurons ↓ ReLU Activation ↓ Dropout — 30% ↓ Softmax Output ``` ⬇️ ### Output ```text 🟢 NO CRISIS OR 🔴 BANKING CRISIS ``` --- ## 📊 Project at a Glance | 🔍 Component | 🛠️ Implementation | | -------------------- | --------- …