Analysing Data and Perfom predictive analysis using Machine Learning models

Computer science and mathmatics student at faculty of science , Interested in data analysis , machine learning and deep learning.
worked as Machine Learning volunteer for several times exprinced how to anlyse data , make it clean and apply different machine learning models.

- In co-operation with my friend Islam Shaker and his team they collect nessarry
data if forms of Excel file to proceed the work
We Developed and Applied Machine learning classification algorithms on a blood
dataset to classify various blood deceases.
Using Feature Engineering and Model selection techniques such as Cross
Validation and Random search we obtained a validation accuracy of 96%

- In co-operation with my friend Safa Magdy and his team they collected an image data about Wheet Rust
We developed a simple CNN model In terms of computation resources we have we couldn't make it completex enough to generalize the data but we achieved 81% Validation accuracy and it was accepted from the School

- In co-operation with my partners from Jordan we worked in a medical images dataset it was multi-label classification problem We used a simple CNN model In addition to various architectuers of Transfer Learning Algorithms including (ResNet 50 - Inception V3 - Xception - VGG 16) to classify images according to three categories(Organ, Modality - Lungitude)

Work Terms

Machine Learning Engineer

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