Hello, I'm Abrha!
Data Analyst and final-year Computer Science student at Mekelle University, specializing in transforming complex healthcare, financial, and operational datasets into clear, actionable insights. Experienced in SQL, Python, Excel, Statistics, Power BI, Tableau, with a focus on building reliable data pipelines and intuitive dashboards. I enjoy helping teams eliminate operational blind spots, streamline routine workflows, and make confident, data-driven decisions supported by clean, well-prepared data.
Programming
Languages
SQL
Python
Data Analyst
Tools
Microsoft Excel
Google Sheets
Google BigQuery
Visual Code Studio
Data Visualization
Tools
Tableau
Power BI
Data Analytic
Methods
- EDA
- Segmentation / Clustering
- Linear Regression
- Logistic Regression
- Statistic
- A/B Testing
Airbnb Booking Analysis | Exploratory Data Analysis (EDA)
An end-to-end analysis focused on uncovering key demand drivers for booking trends. By performing comprehensive data cleaning, outlier handling, and deep bivariate analysis with Pandas & Matplotlib, this project identifies critical factors beyond pricing that directly influence customer booking decisions.
Customer Behavior Analysis
Enterprise retail intelligence solution focused on optimizing consumer engagement strategies. Uses SQL for intricate transaction queries and Python behavioral modeling to construct high-impact Power BI dashboards that successfully uncover high-value purchasing patterns.
Web Scraping and EDA of Job Market Trends in Ethiopia
An automated data mining initiative designed to extract job market insights from etcareers.com for SDG 8. The script parses unstructured web data and transforms it into clean analytical layers to successfully align emerging recruitment fields with regional economic metrics.
House Price Prediction – End-to-End Data Analytics Project
A full-scale real estate analytics project engineered to forecast property valuations. This end-to-end pipeline features intensive feature engineering, data imputation, and advanced model tuning, successfully identifying the primary variables that drive regional pricing trends.
Proactive Child Malnutrition Prediction System
This machine learning framework processes raw physiological measurements to predict acute malnutrition risk in vulnerable populations. By mapping field data into standardized WHO growth z-scores, the predictive system evaluates complex health metrics to enable early clinical decision support.