Varun Mohan

Language-Based Depression Detection

Depression is the most common mood disorder, affecting hundreds of millions worldwide. It can be particularly debilitating in severe cases. For the final capstone project of my Data Science Immersive Program, I created an early detection system, capable of detecting the likelihood of depression indicated from text data.

This system implemented two neural network models. The first was built on vectorized representations of the training data using Bidirectional Encoder Representations from Transformers (bERT) word embeddings, with a Bi-LSTM neural network architecture. The second model was trained on sentiment scores computed for each data point in the text corpus, with a feed-forward network architecture. The models were folded together based on their predicted probabilities of depression, resulting in a more robust combined model, that had 87% accuracy with the validation set.

This was also converted into a web application which can take user input and output the predicted probability of the text being indicative of depression based on the model. The application also includes a 7-day mood-tracker based on the same journal-style inputs for each day. The full project as well as the web application can be accessed respectively by clicking the links below.

Decoding COVID-19

The COVID-19 pandemic has been the world’s most pressing health crisis since the end of 2019. And with so much data being reported and collected every single, it was a subject both extremely relevant and rich for analysis. With this in mind, my team and I set out to conduct a series of analyses of the virus’s progression in the United States, the country which arguably has been hit the hardest.

Among our analyses included a least squares regression which correlated case and death rate data with data from a mask sentiment survey collected by state, to observe the relationship between opinions on wearing masks and the rate of case growth as well as rate of deaths per state. We also analyzed the relationship between political party and case growth, as there is an observable divide in sentiment between parties.

To grant users the ability to quickly visualize COVID metrics for each US state over the entire progression period of the virus, we created a series of web applications with customizable displays. These allowed the users to visualize relative and cumulative case growth per state, view it in choropleth format, as well as compare progression for multiple states. This project and the web application I built can be accessed by clicking the icons below.

Saffron: A Social Media Site for Foodies

The world of social media is exploding, with demand growing faster than any single application can keep up. After the initial boom of the mid and late 2000s that brought about socially focused/topic independent platforms like Facebook and Twitter, social media's ubiquity began to create demand for more specific and specialized platforms related to certain interests, like video games, art, or even professional interest.

So what if we could create a social media platform specifically for food? This was precisely the question that motivated the development of this web application. Saffron is a social media we application built using the Django framework, Python, Javascript, and HTML/CSS. The goal is to create a place for foodies and cooking enthusiants to find each other online and share recipes or casual food-related posts, and create groups based on specific interests. You can access the GitHub repo by clicking the icon below.

Classifying Reddit Posts with Natural Language Processing

Reddit is a massive online community-based platform, containing subreddits on an incredibly diverse array of subjects. My goal for this project was to take two of these subreddits, and see if I could build a classification model capable of understanding which posts correspond to each.

I used the PushShift API to scrape a total of 40,000 posts from the r/PersonalFinance and r/Investing communities. After data cleaning, I created multiple classifiers using either CountVectorization or TFIDFVectorization for preprocessing, and several classification models including Logistic Regression, KNN, Random Forest, and Naive Bayes. Logistic Regression with CountVectorization was the most effective with a validation accuracy of 86%. This project can be accessed by clicking the link below.

Instructional Associate

Selected as instructional associate for 12-week Data Science Immersive Program cohort • Mentored students and provided regular feedback on their code and understanding of data science methods • Assisted and taught lessons in Python, OOP, machine learning, visualization, Git/version control, regressions and classification, introductory statistics, natural language processing, and more

Data Science Immersive

In a 13-week, 480-hour immersive course, gained proficiency with programming in Python and used state-of-the-art data science techniques to solve real-world problems • Developed skills in data cleaning, visualization, statistical analysis, as well as advanced machine learning/deep learning techniques including neural networks, time series analysis, natural language processing, and computer vision

Marketing Analyst

Worked directly with the Chief Marketing Officer to develop and execute B2B and B2C marketing strategies • Managed digital campaigns and conducted weekly performance analysis; implemented changes and improvements based on engagement metrics • Oversaw a 20% increase in engagement from select digital marketing campaigns over a 3 month period • Restructured website landing pages and CTAs based on Google Analytics data to optimize visitor engagement, reducing bounce rates on multiple pages by more than 20%

QA Specialist & GameJam Participant

Designed and developed an original video game for a company hosted “GameJam” event • Worked alongside developers during completion of the project, receiving direct input about the development process • Later hired as a part-time quality assurance specialist. Worked closely with the development team to fine-tune and enhance player experience for 3 projects, one in pre-release stages

Washington University in St. Louis