Overview / Usage

The global refugee crisis is found to be one of the most pressing challenges faced by humanity in recent times. Countless people seek security and safety from challenges such as persecution, war, human rights violation, climate change. The Refugee Status Determination (RSD) process is central to providing shelter to the affected.

Sadly, the RSD process can often be time-consuming and given the gravity of the plight of refugees, it could potentially impact the lives of vulnerable individuals seeking asylum.

The task is to leverage the power of machine learning and data science to enhance the Refugee Status Determination process to promote efficient and accurate decision making. By analyzing the dataset provided by the United Nations High Commissioner for Refugees (UNHCR), we seek to create a classification model that predicts an asylum seeker's likelihood of being granted the status of a refugee.

Methodology / Approach

We followed a straightforward data science pipeline including preprocessing, exploratory data analysis, feature engineering, model building, performance measuring

Technologies Used

Modin, sklearnex, python, jupyterhub, OneDNN, matplot

Documents and Presentations

Repository

https://github.com/iurk1702/intelOneAPI_submission

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Arihant Gupta

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