Ropafadzo-Stix/Financial-Health-Prediction

This is a Zindi Challenge on predicting the financial well-being of small businesses across Southern Africa. # 🏦 SME Financial Health Index - Zindi Challenge > Predicting the financial health of small and medium-sized enterprises across Southern Africa using machine learning. --- ## 📌 Overview This repository contains my work for the Zindi SME Financial Health Index Challenge. The goal is to build a machine learning model that predicts a **Financial Health Index (FHI)** for SMEs — classifying businesses as having **Low**, **Medium**, or **High** financial health. The FHI is a composite measure built across four key dimensions: - Savings and assets - Debt and repayment ability - Resilience to shocks - Access to credit and financial services The dataset covers SMEs from four Southern African countries: **Eswatini, Lesotho, Zimbabwe, and Malawi**, and includes socio-economic and business features such as traded commodities, export/import activity, firm size, demographics, and location. --- ## 🗂️ Repository Structure ``` ├── data/ │ ├── raw/ # Original competition data (not tracked in git) │ └── processed/ # Cleaned and feature-engineered datasets (to be added) ├── notebooks/ │ ├── Starter Notebook.ipynb # Notebook provided by Zindi │ ├── Financial health dataipynb # Contains the complete project i.e., EDA, Processing and Modelling. │ └── 03_modelling.ipynb ├── src/ │ ├── features.py # Feature engineering logic │ ├── train.py # Model training scripts │ └── predict.py # Inference scripts ├── models/ # Saved model artifacts └── README.md ``` > ⚠️ **Note:** This structure is a work in progress and will evolve as the project develops. --- --- ## 🚀 Approach *This section will be updated as the project progresses.* **Current status:** 🟡 In progress Planned steps: - [ ] Exploratory data analysis (EDA) - [ ] Data cleaning and preprocessing - [ ] Feature engineering - [ ] Baseline model - [ ] Model tuning and ensembling - [ ] Final submission --- ## 📊 …