December2024-Present

June 2024 - August 2024

• Taught Fundamental Computer Science concepts (programming basics, algorithms) and guided students through mobile app development projects using App Lab.

• Developed a Story Image Generator Platform using Stable Diffusion XL for image generation and OpenAI GPT-3.5 for story creation.
• Technologies: Python, BLIP, CLIP for fine-tuning, Django for backend, HTML, CSS for frontend.

• Analyzed Spotify Tracks Dataset, performing data cleaning, feature engineering, and model prediction using Python.
• Visualized results on Tableau.
  Project Link

1. Data Import and Preprocessing: Imported the dataset, handled missing values, removed duplicates, and formatted the data.
2. Data Validation: Used Power Query to ensure data accuracy and consistency.
3. Exploratory Data Analysis: Utilized pivot tables to explore relationships, checked for outliers using box plots, and analyzed correlations with a heatmap.
4. Data Visualization: Created visualizations to highlight key insights.
5. Dashboard Creation: Developed an interactive sales dashboard in Excel based on the analysis.

Project Link

1. Data Import and Preprocessing: Imported the dataset, handled missing values, removed duplicates, and formatted the data.
2. Data Validation: Used Power Query to ensure data accuracy and consistency.
3. Exploratory Data Analysis: Utilized pivot tables to explore relationships.
4. Data Visualization: Created visualizations to highlight key insights.
5. Dashboard Creation: Developed an interactive dashboard in Excel based on the analysis.

Project Link

1. Downloaded the dataset.
2. Removed duplicates using Excel.
3. Cleaned data by using "Find and Replace" to standardize values with the same name.
4. Added a new column, Age Brackets, to categorize the original Age column into more useful brackets.
5. Created 3 pivot tables to summarize, aggregate, and visualize the data.
6. Finally, built the dashboard to showcase key insights.

Project Link

• Loaded, explored, and visualized IRIS Dataset. Built KNN, Logistic Regression, and Decision Tree models, compared their results.
• Technologies: Python, Pandas, Matplotlib.
  Project Link

• Supervised Machine Learning (Coursera - Andrew Ng)
• Deep Learning and Neural Networks (Coursera - Andrew Ng)
• Unsupervised & Reinforcement Learning (Coursera - Andrew Ng)
• Data Analysis with Python (Jovian)
• Data Structures & Algorithms (180 Daraga)
• C++ Guides (Udemy)
• Python (Kaggle)
• Git & GitHub (YouTube)
• **Data Analysis by Excel (Udemy)

• Orange Testing Workshop: Learned the basics of software testing.
• Coach Academy Organization: Trained in Data Types, Conditions, Loops, Arrays, and STLs.
• GDSC Machine Learning Workshop: Learned Python and analyzed datasets like Housing Dataset Analysis using Linear Regression and Clustering.
• ICPC-CU Community: Participated as a Bronze Level contestant in problem-solving contests.

• 📋 Resume: Check out My Resume here
• 🎓 Graduation: Cairo University, Faculty of Computers & Artificial Intelligence, AI Department.
• 💬 Ask me about: Data Analysis, Machine Learning, Programming.
• 📫 Reach me at: [email protected]
• ⚡ Fun fact: I love creating data-driven solutions to complex problems!
• 📊 Tableau Projects: Check them out here