MulayeMuhammad/Poverty-Prediction-Nigeria

Predicting poverty levels in Nigeria using CNN and satellite imagery from Kaggle # Predicting Poverty Levels from Satellite Imagery in Nigeria using Deep Neural Networks ## 🌍 Project Overview Poverty remains a major challenge in many developing countries, and access to detailed, up-to-date information is crucial for guiding public policies and humanitarian interventions. Nigeria, as Africa's largest economy, faces significant disparities in living standards, particularly in rural areas. This project leverages **Deep Learning** and **satellite imagery** to develop an automated system for predicting and mapping poverty levels across different regions of Nigeria, providing a cost-effective alternative to traditional household surveys. --- ## 🎯 Objectives 1. **Predict poverty levels** from satellite imagery using Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) 2. **Identify the most affected areas** by poverty across Nigeria 3. **Provide detailed insights** for strategic decision-making 4. **Reduce costs** associated with traditional surveys while increasing analysis accuracy 5. **Create a scalable tool** that can be applied to other countries or regions --- ## 🚀 Why This Matters ### Problem Statement Traditional poverty assessment methods rely on: - **Expensive household surveys** conducted every few years - **Limited geographic coverage** due to accessibility constraints - **Time-consuming data collection** processes - **Outdated information** by the time analysis is complete ### Our Solution By combining satellite imagery with machine learning: - ✅ **Near real-time monitoring** of poverty indicators - ✅ **Complete geographic coverage** including remote areas - ✅ **Cost-effective** compared to traditional surveys - ✅ **Scalable** to other regions and countries - ✅ **Objective and consistent** measurements --- ## 📊 Dataset ### Satellite Imagery - **Source**: Kaggle - Satellite Images to Predict Poverty in Africa - **Dataset**: Nigeria Archive (`nigeria_archive`) - **Coverage**: Multiple regions across Nigeria - …