Salma Elshahawy

Develop a regression model package and publish it to a host platform. Build a full cycle API end-points using Flask API, compose a docker image, and deploy to Heroku. The project following the CI/CD pipelines.

An interactive dashboard build with python and Dash with charts made with plotly to track the covid cases in the United States. The dashboard deployed to Heroku Paas platform for easy access and sharing.

A Kaggle competition to predict how capable each applicant is of repaying a loan. The objective of this model is to use historical loan application data to predict whether or not an applicant will be able to repay a loan. Manual feature engineering has been used by utilizing advanced pandas data manipulation and aggregation functions.

Utilized big data to develop a fully functional book recommendation using ML Pyspark library. The model uses the CBF to recommend books to new users. The output recommendations are stored in a big JSON format for fast integration into existing applications.

CRUD application in Ruby on Rails as backend, javascript as a frontend and deployed to Heroku.

Utilized different advanced types of regression to predict the final price of each home with more than 70 explanatory independent variables.

The goal of this project was to build a spam filter that can effectively categorise an incoming mail or text message as either spam or ham.

Automating the loan eligibility process (real time) based on customer detail provided while filling online application form. A comparasion between different ML algorithms have been conducted before applying to pick the most efficient one.

Utilized the BeautifulSoup library to scrape tutorials published on the Datacamp community edition. Then wrote the results into a comma-separated file (CSV) for further analysis.

CRUD application in Ruby on Rails as backend, javascript as a frontend and deployed to Heroku.