Assem Salama

Assem Salama

Data Science Practitioner

Portfolio

Market Forecasting in India

This project is providing a tool for financial professionals and investors to predict and manage stock market volatility in the Indian market. By creating a model to forecast volatility, individuals and organizations can make more informed investment decisions and potentially reduce the financial risks associated with market fluctuations. This can be particularly valuable for traders, portfolio managers, and investors looking to optimize their strategies and minimize losses.

A/B Testing at WorldQuant University

This project aims to address the challenge of low completion rates for the admissions quiz in the Data Science Lab (DS Lab) program at WQU. The primary business problem is a substantial number of applicants not finishing the quiz, which can lead to reduced enrollment, inefficient resource allocation, and potential impacts on the quality of admitted students. The expected outcomes include increased quiz completion rates, resulting in higher enrollment, better resource management, and improved student quality, ultimately benefiting the DS Lab program at WQU.

Customer Segmentation in the US

This project leverages 2019 Survey of Consumer Finances data and unsupervised learning to identify credit-constrained households. With broad implications, it empowers financial services, marketing, credit scoring, policymakers, and researchers to enhance financial inclusion, precision, and understanding. Its goal is to boost access, engagement, and informed choices while aiding underserved households.

Bankruptcy in Poland

This project builds predictive bankruptcy models using financial data from Poland and Taiwan, aiding investors in making informed decisions, helping financial institutions assess credit risk, and promoting stable business partnerships. It also assists policymakers in enhancing financial regulations and provides valuable insights to researchers, contributing to financial stability in these regions.

Earthquake Damage in Nepal

This project presents a critical solution for assessing building damage caused by the Nepal 2015 Earthquake, benefiting disaster response and recovery. By analyzing data from Gorkha and Ramechhap districts through Open Data Nepal, it equips authorities and organizations with valuable insights for targeted interventions and resource allocation. Moreover, it supports the development of earthquake-resistant construction methods, strengthening the overall resilience of communities in these vulnerable areas.

Air Quality in Nairobi

This project provides a valuable solution for improving air quality management in major African cities, namely Nairobi, Lagos, and Dar es Salaam. Leveraging data from openAfrica, we developed a time series model to predict PM 2.5 readings throughout the day, offering actionable insights for local governments, environmental agencies, and health organizations. It empowers decision-makers to implement targeted interventions, reduce pollution levels, and enhance public health, addressing critical air quality concerns across these urban centers.

Housing in Buenos Aires

In this project, we acquired data wrangling and visualization skills while examining the real estate market in Mexico. We built on those skills and transitioned to descriptive and predictive data science. Our focus was on real estate, where we created a machine-learning model that predicted apartment prices in Buenos Aires, Argentina.

Supply Chain Operations at Faasos

This project explores the fictional food delivery service, Faasos, using a SQLite database. It covers three main areas: database setup and data insertion, answering a wide range of business questions, including roll metrics and driver/customer experience. The project showcases the ability to clean and analyze data, create SQL queries, and derive valuable insights.