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Isadiki/financial-inclusion

Domain:

socioeconomic

Record type:

project
Creator:
Isa
Host:
Data analysis project on banking access, trends & ML for financial inclusion in South Africa πŸ“Š 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 πŸ“ Project Structure bash Copy Edit financial-inclusion/ β”œβ”€β”€ data/ β”‚ └── cleaned_financial_data.csv β”œβ”€β”€ notebooks/ β”‚ └── financial_analysis.ipynb β”œβ”€β”€ dashboards/ β”‚ β”œβ”€β”€ powerbi_dashboard.pbix β”‚ └── 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 πŸ“Š Power BI Dashboard Key insights: % unbanked by age & gender Credit access vs. income level Map of underserved provinces πŸ’‘ Screenshot goes here (add .png file to dashboards/ folder) πŸ“ˆ Tableau Dashboard A storytelling dashboard highlighting: How financial access has changed over time Correlation between education and credit access πŸ’‘ Add your Tableau screenshots or embed GIF previews πŸ€– 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 πŸ’¬ 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 πŸ™‹β€β™€οΈ Author Ivy Sadiki Senior Test Analyst | Data & A …

Visit

github.com

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