Ming Senn Teo

📈 Personal Projects

• Bank Marketing Database Design
• Personal Website

🐼 Class Projects

Unfortunately, I cannot publish the source code for academic projects on the Internet (with some exceptions). If interested in learning more, please contact me.

_{Abbreviations:
ML (Machine Learning) - EDA (Exploratory Data Analysis) - SWE (Software Engineering and Optimization) - DB (Database)}

ECON 148: Data Science for Economists (Spring 2024)

1. [ML] [EDA] Ted Miguel’s Paper Reproduction
   • Reproduced the analysis of a paper by Edward Miguel by translating the original paper’s code from Stata to Python (Paper: Non-economic factors in violence: Evidence from organized crime, suicides and climate in Mexico)

DATA 102: Data, Inference, and Decisions (Spring 2024)

1. [ML] [EDA] An Investigation into the Causality of Arkansas Speed Limit Increase on Traffic Accidents and the Effect of Road Conditions on Traffic Accident Duration
   • Analysis of traffic accident patterns over a period where there’s an increase in speed limit for the state of Arkansas, determining the causality behind it

DATA 144: Data Mining and Analytics (Fall 2023)

1. [ML] [EDA] Traffic Accident Delay Prediction
   • Analyzed accident patterns in the US Traffic Accidents through EDA and built a classifier that predicts the level of delays that an accident caused.

DATA 101: Data Engineering (Fall 2023)

1. [DB] Query Optimization (PostgreSQL)
   • Explored various query optimization techniques and learned how to further tune query performance on Lahman’s Baseball Database
2. [DB] Data Transformation (PostgreSQL)
   • Utilized multiple data transformation techniques to clean and impute missing data on one month of sensor data from buildings at UC Berkeley Dataset
3. [DB] MongoDB
   • Compared different database systems on their handling of semi-structured JSON data using Yelp Academic Dataset

DATA 100: Principles and Techniques of Data Science (Spring 2023)

1. [ML] Housing Sales Price Predictor
   • Built a linear regression model that predicts the housing prices in Cook County using feature engineering and cross-validation (500000+ entry housing database)
2. [ML] Spam Classifier
   • Built a logistic regression model to predict whether an email was spam or ham. Received a 94% accuracy on the testing set.

DATA 8: Foundations of Data Science (Fall 2022)

1. [ML] Movie Genre Classifier
   • Classifies movie genres using KNN Model on common word frequencies

DATA C88C: Computational Structures in Data Science (Fall 2022)

1. [ML] Maps
   • Yelp Recommender. Recommends highly rated restaurants to users by categorizing restaurants by their rating. Combines basic supervised and unsupervised learning and object-oriented programming paradigms.
2. [SWE] Ants vs. SomeBees
   • Spin off of the popular game Plants Vs. Zombies. Combines functional and object-oriented programming paradigms.

CS 61B: Data Structures (Fall 2022)

1. [SWE] Build Your Own World
   • Built a simple 2D game that involves random world generation with seeds, interactions between the user and the game world, a complete user interface, and other basic game features such as progress saving and visual effects.