The problem NoLapse solves

The idea here is that we want to evaluate / analyze the factors that can theoretically contribute to a patient’s potential relapse pertaining to substance abuse. By analyzing existing SAMHSA data spanning 1992 - 2021, the team opted to create a predictive model that can accurately identify the most significant correlative factors for relapse. This, in conjunction with a variety of Tableau visualizations, provides valuable insight into America’s current substance abuse crisis and potential future actions to mitigate its consequences.

Challenges we ran into

The most prominent challenges involved locating and aggregating national health datasets pertaining to rehabilitative treatment that corresponded to our project’s particular use case and stated objective. Firstly, it is important to note the discordant practices and ethical health standards between different states as well as the prevalence of null values, where certain governments and institutions omit the collection of data in certain key areas, including education, employment status, waiting times before treatment, and others. Additionally, many of the datasets the team encountered were formatted such that values were encoded in a way that became difficult to interpret and/or visualize in a digestible manner.