aruna-rajeswari/EastAfricaFinancialAnalysis

This project delivers an end-to-end financial inclusion analysis pipeline for East Africa—combining Python-based data cleaning, SQL-driven analytics, and Power BI visualizations to uncover demographic, income, and access-to-finance patterns in a large synthetic dataset. # 🌍 East Africa Financial Analysis Using Python, SQL and PowerBI ## 📌 Project Overview This project aims to build an **end-to-end data engineering & analytics pipeline** to study **financial inclusion across East African countries** using a synthetic dataset of 50,000 rows. It simulates real-world data challenges by including missing values, logical inconsistencies, negative income values, and malformed categorical fields. ### 🔧 Core Components 1. **Python Data Pipeline** — cleaning, transforming, validating data 2. **SQL Database Integration** — storing and analyzing cleaned data 3. **Data Visualization** — insights using Python (Matplotlib/Seaborn) or Power BI --- ## 📊 Dataset Description The dataset includes **50,000 records** representing demographic and financial inclusion details. | Column | Description | |-------------------|-------------| | `age` | Age of the individual | | `gender` | Gender (with missing/malformed entries) | | `country` | Country of residence | | `education_level` | Highest education achieved | | `has_bank_account`| Bank account ownership (inconsistent labels) | | `mobile_money_user` | Mobile money usage | | `loan_access` | Access to credit or loan facilities | | `monthly_income` | Reported monthly income (contains negative values) | # 🚀 Project Phases ## **Phase 1: Python Data Pipeline** The goal is to build a robust pipeline that cleans and transforms the dataset. ### ✔ Tasks Include: - Load raw data using **pandas** - Identify & fix logical errors: - Replace invalid age values - Normalize `has_bank_account` (yes / no / unknown → 1 / 0 / NaN) - Remove or impute negative incomes - Standardize gender values - Clean missing entries - Save the cleaned dataset to a new CSV file --- ## **Phase 2: SQL Integration** The cleaned dataset is imported into **SQL** for analysis. ### 🔍 Key SQL Queries include: - **Count of users per country** - **Average income by education level** - **Correlati …