About Me
IT Student at Egyptian E-Learning University (EELU) with a strong passion for Artificial Intelligence, Computer Vision, and Neural Networks. Hands-on experience in data preprocessing, exploratory data analysis (EDA), predictive modeling, and building interactive web apps using Python, Streamlit, and Scikit-Learn, as well as designing insightful Power BI dashboards.
Skills & Tools
Selected Work
Hair Loss Documentation
An exploratory data analysis (EDA) and preprocessing pipeline built in Python to analyze 400 days of personal health and habit tracking data. Key Features & Workflow: • Data Cleaning: Processed a 14-feature dataset, handled missing values, and dropped non-informative columns. • Categorical Encoding: Applied LabelEncoder to transform lifestyle, stress, and scalp metrics into machine-learning-ready numeric formats. • Statistical EDA: Conducted univariate distribution and variance analysis across core features using Matplotlib & Pandas. • Targeted Insights: Analyzed key variables including stress levels, coffee intake, sleep patterns, and scalp conditions to identify health trends. Tech Stack: Python, Pandas, NumPy, Scikit-Learn, Matplotlib.
Customer Demographics & Loan Analytics
Interactive Customer Demographics & Loan Analytics Dashboard designed to analyze financial profiles, loan volumes, and customer distribution across regions and age groups. Key Features & Insights: • Customer & Financial KPIs: Tracked total customer base (~47.45K) and first loan amount distributions. • Demographic Segmentation: Analyzed customer density by geographic region (North, South, Central) and age brackets (highlighting peak activity in ages 35-54). • Gender & Income Breakdown: Visualized income counts and distribution patterns categorized by gender. • Credit Bureau & Agency Matrix: Tabulated cross-analysis comparing credit agencies (CIB, CRIF, EXP) across different customer age cohorts. • Data Visualizations: Implemented TreeMaps, Bar Charts, Donut Charts, and Matrix Tables for multi-dimensional filtering. Tech Stack: Power BI, DAX, Data Modeling, Data Visualization.
Work Experience
achine Learning Trainee
National Telecommunication Institute (NTI)
- •Completed intensive hands-on training in Machine Learning and Data Science fundamentals.
- •Applied data preprocessing, feature engineering, and exploratory data analysis (EDA) techniques using Python.
- •Built and evaluated machine learning models using Pandas, NumPy, and Scikit-Learn.
- •Developed practical data projects focused on predictive modeling and analysis.
Machine Learning Trainee
Google Developer Groups (GDG)
- •Participated in technical workshops covering Machine Learning and AI algorithms.
- •Applied Python for data manipulation, visualization, and building predictive models.
- •Collaborated on hands-on practical tasks and data analysis workflows.
Certifications
Machine Learning Workshop Certificate
oogle Developer Groups (GDG) · Apr.2026
CCNA: Introduction to Networks
Cisco · 17 May 2026