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Objective
  • •I'm passionate about Machine Learning and Deep Learning engineering. Seeking hands-on experience in the field, I am eager to apply my knowledge and skills to real-world projects and collaborate with interdisciplinary teams on model development. With my strong communication skills, I aim to translate complex data insights into actionable solutions, driving innovation and impact in the field of machine learning. Additionally, I am committed to continuous learning with the goal of becoming a computer vision engineer.
Skills
Programming
  • •Python
Data Visualization
  • •Matplotlib, Seaborn, Plotly
Frameworks | Libraries
  • •Pandas, TensorFlow, Scikit-learn, Keras, OpenCv, StreamLit
Projects

Bank Customer Churn

Data Analysis | Machine Learning
  • •To identify customers who are likely to churn so that appropriate actions can be taken to retain them.
  • •Applying various algorithms like Decision Tree, Random Forest, AdaBoosting, Gradient Boosting, XGBoosting

    • Phones Recommendation System

  • •Designed and implemented the Content-Based Filtering algorithm, which leveraged phone attributes to generate personalized recommendations for users.
  • •Deployment by using Streamlit.

    • Brain Tumor Classification

    Deep Learning
  • •Classifying MRI into different categories based on specific criteria. By harnessing the power of machine learning techniques, such as convolutional neural networks (CNNs) and deep learning.
  • •By using data preprocessing like data augmentation and using transfer learning model like MobileNet.

    • US Car Accidents

    Data Analysis
  • •The US Car Accident analysis project is a data-driven exploration aimed at understanding the patterns, trends, and factors contributing to vehicular accidents across the United States. By analyzing a comprehensive dataset containing detailed information about accidents, including location, weather conditions, road features, and severity, this project seeks to uncover insights that can inform strategies for improving road safety and reducing accident rates nationwide.

    • Microsoft Stock

    Time Series | Machine Learning
  • •Analyzing the time series data of Microsoft's stock prices over a specific period, through statistical analysis and machine learning techniques.
  • •Applying ARIMA Model, XGBoosting.

    • IBM HR

    Data Analysis
  • •Analyzing a rich dataset encompassing employee demographics, job satisfaction metrics, performance evaluations, and tenure, this project seeks to uncover patterns and trends that can inform HR strategies to enhance employee engagement, satisfaction, and retention within the organization.

    • Car Price Prediction

    Data Analysis | Machine Learning
  • •Exploratory Data Analysis models of cars to gain insights into the dataset, and applying inferential statistics to understand the significance of these observations.
  • •Applying Machine learning algorithm like Linear Regression to make predictions.

    • Rotten and Fresh Classification Fruits

    Deep Learning
  • •Analyzing images of fruits to determine their freshness status. By training on a dataset containing labeled images of both fresh and rotten fruits, the deep learning model learns to recognize visual patterns indicative of freshness or spoilage.
  • •Using Transfer Learning Model.
    • Education
    2018/07 – 2022/07October, Egypt

    B.S. Computer Science and Information System

    City of Culture and Science University