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Isadiki/financial-inclusion

Domaine:

socioeconomic

Type de record:

project
Créateur:
Isa
Hôte:
Data analysis project on banking access, trends & ML for financial inclusion in South Africa 📊 Financial Inclusion in South Africa This project investigates financial inclusion trends in South Africa using open financial datasets. It includes data cleaning, analysis, dashboards, and a machine learning model to predict loan default risk. 🧰 Tools & Technologies Python (Pandas, Seaborn, Scikit-learn) Power BI (interactive dashboard) Tableau (storytelling dashboard) Jupyter Notebook Open financial datasets (Global Findex, SARB, Stats SA) 🎯 Project Objectives Understand disparities in access to banking & credit Identify socio-economic factors behind exclusion Predict likelihood of loan default using ML Present findings through clear visual dashboards 📁 Project Structure bash Copy Edit financial-inclusion/ ├── data/ │ └── cleaned_financial_data.csv ├── notebooks/ │ └── financial_analysis.ipynb ├── dashboards/ │ ├── powerbi_dashboard.pbix │ └── tableau_dashboard.twbx └── model/ └── loan_default_model.py 🧼 Data Cleaning Performed in Python: Filled missing income/employment values Removed duplicates & irrelevant columns Categorical encoding and normalization 📊 Power BI Dashboard Key insights: % unbanked by age & gender Credit access vs. income level Map of underserved provinces 💡 Screenshot goes here (add .png file to dashboards/ folder) 📈 Tableau Dashboard A storytelling dashboard highlighting: How financial access has changed over time Correlation between education and credit access 💡 Add your Tableau screenshots or embed GIF previews 🤖 ML Model – Loan Default Prediction Python model using: Logistic Regression & Decision Trees Features: income, employment, age, credit history Output: likelihood of default bash Copy Edit # Run model python model/loan_default_model.py Result: ~82% accuracy on test data 💬 Key Findings Youth (18–25) and informal workers are underbanked Education strongly correlates with financial access Default risk is higher among low-income, unemployed users 🙋‍♀️ Author Ivy Sadiki Senior Test Analyst | Data & A …