sith0008/Malawi-Flood-Prediction

# Malawi-Flood-Prediction This project is done as part of the UNICEF Arm 2030 Vision #1: Flood Prediction in Malawi competition hosted on Zindi, an African data science competition hosting platform. ## Dataset Each row of the original dataset corresponds to a Square, which is an area in Southern Malawi. Each square has the following data: - 17 weeks of rainfall data prior to the floods - elevation - land type - flooding percentage (target variable) ## Supplementary datasets used For a more in-depth exploratory data analysis and to generate more features, I made use of the following datasets: - polygons of administrative regions in Malawi (data.humdata.org) - polygons of water bodies in Africa (energydata.info) ## Exploratory Data Analysis The first EDA notebook documents the relationships between the given features (rainfall, elevation, land type and flooding percentage). The second EDA notebook aggregates Squares into Level 3 administrative regions and looks into the rainfall, elevation and flooding characteristics in each administrative region. ## Feature engineering On top of engineering features from the original dataset, I derived more features for each Square to help with the prediction: - aggregated rainfall data - relative amount of rainfall (compared with mean rainfall in the administrative region) - relative elevation (compared with mean elevation in the administrative region) - number of water bodies in the same administrative region - distance to closest water body ## Modelling I experimented with several regression techniques and the results are as follows: | Model | Lasso | Ridge | Random Forest | Lightgbm | Multi-Layer-Perceptron | | :---: | :-: | :-: | :-: | :-: | :-: | | RMSE | 0.2183 | 0.2183 | 0.1226 | 0.1167 | 0.0964 | I created a new dataset using the predictions from …