Chicago Crime Data Cleaning, SQL Analysis and Power BI Visualization:
I independently completed a crime data analytics project using Chicago crime records to examine crime patterns, arrest trends and geographic concentrations.
The project involved cleaning and transforming raw crime records using MySQL, designing a relational database structure and developing analytical reports using SQL and Power BI.
Key activities included:
Cleaned and transformed Chicago crime records using SQL.
Created structured relational tables for criminal cases, crime classifications and geographic information.
Identified missing values and duplicate records.
Used SQL queries to analyze crime frequency and arrest patterns.
Examined crime distributions across categories and locations.
Developed Power BI dashboards to communicate analytical findings.
Key findings:
Battery was the most frequently recorded crime category.
Simple domestic battery accounted for 8.21% of recorded incidents.
Approximately 15.48% of recorded incidents resulted in an arrest.
Battery represented 22.92% of arrests, followed by narcotics at 17.22%.
Tools: MySQL, SQL, Power BI and DAX.
Deliverables: Cleaned datasets, relational database tables, SQL scripts, analytical findings and interactive Power BI dashboards.
This project demonstrates my ability to work with large public datasets, perform structured data analysis and communicate findings through visual reporting.
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I independently completed an end-to-end data analytics project using Kenya's public procurement data to examine procurement patterns, contract awards, supplier participation and procurement performance.
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