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Analyzed controversy in Subreddits and designed a data visualization dashboard to display results
- Modified and maintained Subreddit data scraper with Asyncpraw
- Designed pipeline to preprocess subreddit data and feed into BERT model to get topics and sentiment analysis on subreddits
- Wrote a module to get comment relevancy to their post
- Designed “controversy” metric from topics, sentiment, and relevancy to rate “controversy” of a subreddit
- Developed data visualization dashboard to show results of topics, sentiment, relevancy, and controversy using Plotly Dash
Our goal is to analyze the five shilling attack methods: Random Attack, Average Attack, Bandwagon Attack, Segmented Attack, Sampling Attack on different recommender systems. Recommender models chosen for evaluation are: User-based KNN model, Item-based KNN model, Latent Factor model, Neural Matrix Factorization model
- Implemented functions for Random Attack, Sampling Attack, and Segmented attack
- Implemented Neural Matrix Factorization model and trained on different attacked data sets
- Discovered KNN and Latent Facor recommender systems are more vunerable; Neural Matrix Factorization is the most robust against attacks
My goal for this project was to benchmark Azure and AWS spark sessions.
- Implemented a K-Means function for spark RDDs
- Ran the running time of K-Means on Azure and AWS with varying sizes of data and number of workers
- Discovered that Azure spark has a slightly faster running time than AWS
- Discovered size of data impacts running time linearly and number of workers impacted running time exponentially
A project that can train people to learn correct postures for sports. This project uses computer vision to learn the correct postures from a professional sports player. A student can try to mimic the professional sports player's movement, and the program will give a score for how well they performed in comparison.
- Built front-end flask app to upload trainer video and watch students live
- Extracted human body positions using mediapipe
- Implemented and refactored functions to recognize poses from trainers and students
Recipe recommender for Meal Planning based on nutrition and available ingredients.
- Developed end-to-end Multi-Touch Attribution Model to attribute credit to marketing campaigns for a customer purchase
- Designed modules to query data, clean the data, model the data, and generate a visualization for the user
- Saved marketing spending on underperforming marketing campaigns while maintaining optimal customer purchase
- Designed and developed a trend anomaly diagnostics system which assisted in understanding how and why an anomaly occurred; enabling users to interpret and take action when facing a detected trend anomaly alert
Find which government policies were the most effective at slowing down COVID-19 cases
- Searched and scraped data online regarding COVID-19 cases and government policies implemented in the United States
- Cleaned the data into a usable format for machine learning algorithms by using Pandas package from Python
- Analyzed government policies from generated auto-regression time series and SAS JMP graphic visualizations and removed nonimpactful
government policies from the regression
- Discovered the most impactful government policies were mask mandates and gathering restrictions of any size
Created an Android App to play chess with another human player or a bot
- Designed graphical user interface using Android Studio
- Developed code to play Chess through console output
- Integrated the Android graphical user interface to be compatible with Chess code
- Enhanced App by adding extra functionality such as playing against AI, saving games, and recording previously played games