aisha13dikko-sudo/AISD-poverty-Nigeria-Context

COMP0173 AI for Sustainable Development Coursework 2 – POVERTY PREDICTION IN NIGERIA USING GEOSPATIAL AND HOUSEHOLD INDICATORS # AISD Poverty Prediction – Nigeria Context (UCL MSc AI for Sustainable Development) This repository contains the code used to generate the results presented in my coursework poster: **“Poverty Prediction in Nigeria Using Geospatial and Household Indicators”** ## Notebooks - **01_Baseline_Replication.ipynb** Reproduces the approach from Jean et al. (2016) using proxy satellite-based features. - **Nigeria_tabular_model.ipynb** Implements the adapted XGBoost model using LSMS 2018 household tabular features. Includes: - data preparation - log-transform of consumption - train/validation/test split - model training - R² and RMSE evaluation - predicted vs actual plot - **01_env_check.ipynb** Simple environment test (not required to run models). ## Data Availability The LSMS 2018 dataset is **not included** in this repository due to licensing restrictions. Notebooks assume the cleaned dataset is available locally: ```python cons = pd.read_csv("totcons_final.csv") hh = pd.read_csv("nga_householdgeovars_y4.csv") df = cons.merge(hh, on="hhid", how="inner") ``` ## ⚙️ Environment The analysis uses the following Python libraries: pandas numpy scikit-learn xgboost matplotlib Tested in Google Colab 📊 Poster Outputs from these notebooks (figures,and performance metrics) appear in the accompanying poster for: Coursework 2 — AI for Sustainable Development, UCL The poster summarises: The adaptation of the Jean et al. (2016) methodology The improved performance of LSMS + XGBoost SDG alignment Ethical reflections