My Projects

 Custom Bags
Customer Service Management System

Our system delivers streamlined product management with precise order tracking and automated expense calculation, saving you valuable time and effort. It also offers clear customer review analysis and a fast, intuitive interface designed for everyone.

Development Tools :

HTML5 , CSS3 , JavaScript , Python , Django , Sql Lite 3


Project
Salaries Analysis

This project analyzes a dataset containing detailed information on San Francisco city government employee compensation—covering job titles, departments, base salaries, overtime, and benefits. The analysis reveals trends such as skewed salary distributions.

Development Tools :

Python, Pandas, Numpy, Seaborn, Matplotlib


 House Analysis Project
House Prices Predictions

About This project aims to predict house prices based on various features such as area, number of bedrooms, bathrooms, access to main road, and more. The model uses linear regression and standard scaling for input data.

Development Tools :

Python, Pandas, Scikit-learn,Numpy, Seaborn, Matplotlib, Stremalit,


Project
Egypt’s Premier Fashion Sales Analysis

Egypt’s Premier Fashion Sales Analysis is a data-driven project that provides interactive sales insights using Streamlit, Power BI, and Pandas. It analyzes sales trends, customer behaviors, and product performance while offering real-time dashboards and downloadable reports. helping businesses make informed, data-driven decisions.

Development Tools :

Python, Pandas, Numpy, Seaborn, Matplotlib , streamlit


 Custom Bags
Home Store E-commerce store

roject Overview Home Store is a web-based e-commerce application developed using the Flask framework. It allows users to browse products, manage their shopping cart, and place orders. The application also includes administrative functionalities for managing products and orders.

Development Tools :

HTML5, CSS3, Bootstrap , JavaScript, Python, Flask , Sql Lite 3


 House Analysis Project
Titanic

About This project aims to predict house prices based on various features such as area, number of bedrooms, bathrooms, access to main road, and more. The model uses linear regression and standard scaling for input data.

Development Tools :

Python, Pandas, Scikit-learn,Numpy, Seaborn, Matplotlib, Stremalit,