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Hey šŸ‘‹, Here is Mazen Hassan

I graduated in Communication and Information Engineering at Zewail City, Believing in life long learning journey, and a TensorFlow Certified Developer from Google with 3 Years of experience in the Data Science Field, Seeking a challenging opportunity to improve my skills in the Data Science Field and specifically Deep Learning. .
  • šŸŒ± Iā€™m currently learning: Deep Learning

  • šŸ’¬ Ask me about Python, R, Statistics, Numpy, Pandas, Matplotlib, SQL, Keras & TensorFlow.

  • šŸ“« How to reach me [email protected]

Languages and Tools:

aws c docker gcp git kubernetes matlab mssql mysql OpenCV python scikit_learn tensorflow

Find me around the web:

ahmedlila zasore medolela

mazenhassan9

Mazen Hassan's Projects

100-days-of-code icon 100-days-of-code

Fork this template for the 100 days journal - to keep yourself accountable (multiple languages available)

alx-pre_course icon alx-pre_course

I'm now a ALX Student, this is my first repository as a full-stack engineer

alx-zero_day icon alx-zero_day

I'm now a ALX Student, this is my first repository as a full-stack engineer

cross-validation icon cross-validation

1)To compare and verify which model is better in which situation. 2) Clean the data, Hyper Tuning both models, Building function to evaluate the models, training validating the model and comparing, then applying K-fold to see the improvement. 3) It's been showed how LightGbm is faster than XGboost, however, XGboost works better with lower data than LightGbm as it trains better, and when applying K-fold it increases the accuracy more and more.

deep_learning_explorations icon deep_learning_explorations

Codes and experiments while learning and exploring deep learning for personal curiosity by doing online courses, personal projects and work.

fashoinmnist icon fashoinmnist

A multi Classifier Model used to classify the 10 classes of the Fashion Mnist Dataset using CNN's

imagenet icon imagenet

A multi Class Classifier for Flowers Classification using Pre-Trained Model ImageNet

importance-of-the-parameters icon importance-of-the-parameters

1) The Goal was to create a Machine learning Random Forest Algorithm Regerisor. 2) I've cleaned the data from null, missing and categorical values, made it more applicable for the model. 3) choosing the model and hyper tuning it, 4) testing the importance of the parameters, then dropping the useless parameters in order to make the model more efficient. 5) The model achieved 93.3% accuracy with all parameters while achieving 93.2% after dropping all the useless and impacted a huge improvement in performance.

vessel-rest-api icon vessel-rest-api

what we call a trade consist in a commercial exchange between two entities. The first one being the seller, the second one the buyer. Each of these entities are located in a country. A trade starts and ends in a country location, from and to a specific date. It involves a vessel which carry a specific commodity identified in the following tree : group -> family -> product.

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