Divyareddy45/East-Africa-Financial-Analysis

**🌍 Financial Inclusion Data Pipeline & Analytics (East Africa)** **📌 Project Objectives** Clean and transform a messy, realistic financial dataset using Python (pandas) Store and query the cleaned data using SQL Explore and communicate financial inclusion insights via visualisations Power BI Demonstrate a practical, production-style data pipeline workflow **🧾 Dataset Description** The dataset contains 50,000 rows of synthetic financial data, including intentional data quality issues (e.g. negative incomes, unrealistic ages, inconsistent categories) to simulate real-world conditions. Financial Inclusion Project Key Columns: | Column | Description | | ------------------- | ---------------------------------------- | | `age` | Age of the individual | | `gender` | Gender (with missing / malformed values) | | `country` | Country of residence | | `education_level` | Highest education level achieved | | `has_bank_account` | Bank account ownership status | | `mobile_money_user` | Whether the individual uses mobile money | | `loan_access` | Access to credit or loans | | `monthly_income` | Reported monthly income | **🔄 Pipeline Overview 🔹 Phase 1 – Data Cleaning in Python** The goal is to build a pipeline that clean and transform the dataset **Task**: • Load the dataset using pandas. • Identify and fix logical errors: o Replace invalid age values. o Normalize has_bank_account field (yes, no, unknown → 1/0/NaN). o Remove or impute rows with negative income. o Handle missing values. • Save cleaned data as a new CSV. **Phase 2: SQL Integration** • Import cleaned data into an SQLite or PostgreSQL database. • Write SQL queries to: o Count users per country. o Average income per education level. o Correlate bank account ownership with mobile money usage. o Segment users by financial inclusion …