Dr. Mohamed Gabr
Data Science Manager & Artificial Intelligence Product Owner
Although, I have done various information management jobs and even
business development, consulting and presales jobs in different businesses
and industries, I prefer to consider myself as a Data scientist and
collaborative Business Intelligent (BI) analyst. A senior data scientist
(IBM Certified with, MBA in technology management), a Google Analytics
certified professional (GAIQ) with 15+ years of hands-on experience and
skills in data modeling, data analytics, data mining, and machine learning
algorithms with focus on attribute and spatial/ geographic data.
Please visit my LinkedIn Profile and Github Page
This data product is a part of a project for creating a smart city monitoring and prediction dashboard. The project focused on the aspects of traffic volumes during the day, traffic video surveillance,
air quality, sea water quality, power consumption levels, weather conditions, and preventive maintenance for IOT sensors.
All the big data pipelines have been created, the data have been cleaned, and many models have been built to satisfy the client's requirements.
This data product has been prepared as a proof of concept of a machine learning model to predict prices of houses in Cairo, Egypt. For demonstration purposes, we have used the data from Bangalore to prove the technical feasibility of the model. Developing the final model required many steps following the CRISP-DM methodology. After building the model we used it to predict the prices in this application. The model can be changed/ enhanced for any another city based on its own data.
This data product has been prepared as a proof of concept of a machine learning model to predict prices of houses in Cairo, Egypt. For demonstration purposes, we have used the data from Boston to prove the technical feasibility of the model. Developing the final model required many steps following the CRISP-DM methodology. After building the model we used it to predict the prices in this application. The model will also show the contribution (weight) of each parameter in predicting the price. The model can be changed/ enhanced for any another city based on its own data.
This data product has been prepared as a proof of concept of a machine learning model to predict breast cancer. Developing the final model required
many steps following the CRISP-DM methodology. After building the model we used it to predict the disease in this application. The model can be changed/
enhanced for any another population based on its own data.
This data product has been prepared to be used as a practical project in the training courses provided by Dr. Mohamed Gabr. Developing the final model required many steps following the CRISP-DM methodology. After building the model we used it to predict the Penguin Speciesr type in this application.
This data product has been prepared to be used as a POC for sports analytics. First, the data has been scrapped from the web. Then grouped by year, team , and position of the player to analyze the player's performance in rushing. The same process can be used to analyze other topics like Receiving, Scrimmage Stats, Defense, Kicking & Punting, Kick & Punt Returns, Scoring, and others.
This data product has been prepared to be used as a practical project in the training courses provided by Dr. Mohamed Gabr. Developing the final model required many steps following the CRISP-DM methodology. After building the model we used it to predict the iris folower type in this application.
This data product has been prepared to be used as a POC for sports analytics. First, the data has been scrapped from the web. Then grouped by year, team , and position of the player to analyze the player's performance in rushing. The same process can be used to analyze other topics like Receiving, Scrimmage Stats, Defense, Kicking & Punting, Kick & Punt Returns, Scoring, and others.
