Data Analyst with a background in Middle East Policy and a passion for innovative problem solving and critical thinking. Understanding of building SQL databases, Machine Learning, and Big Data while integrating strong fundamental statistical understandings. With previous experience in the government sector and policy making, I excel at delivering highly complex information to audiences unfamiliar with the subjects at hand and offering actionable results. Bilingual Arabic and English.

• 🔭 I’m currently working @ Mathematica
• 🌱 I’m currently advancing my skills in SQL, Tableau, R, Machine Learning, and Python
• 💬 Ask me about Politics (specifically Middle Eastern Affairs), Sports, and all things Data Analysis
• 😄 Pronouns: He/Him
• 🏛 Washington, D.C.

🇰🇷 Time Series Analysis of Air Pollution in Seoul, South Korea

• Collected 650,000 data points across 25 districts of the daily average of each pollutant level.
• Feature Engineered to filter for areas that reach levels of extremely dangerous pollutants
• Studied the World Health Organization’s standards of such pollutant and filtered further
• Modeled over the top 5 districts with the highest pollutants and predicted levels for days later in the interest of public health concerns.

🛏️ Zillow Real Estate Analysis

• Obtained home values per zipcode from Zillow’s website from the years of 1996-2018.
• Filtered and calculated annual return rate to find the top five zip codes with the highest return rate in Texas.
• Forecasted values for the top five zip codes through filtering and Time Series Modeling.

🚗 Predictive Modeling of Car Crashes in Chicago

• Feature engineered and filtered to enhance model performance and hone in on relevant functions.
• Create a series of models through grid searches, cross-validation in logistic regressions, random forests, decision trees, and xgboost classifiers to find the most optimized model.
• Filtered for proper feature priorities from models to inquiry what factors best predicted severe crashes and fatalities in Chicago.