Hi, I’m Migs Germar.
Welcome to my data science blog. Feel free to explore my posts below. I recommend checking out My Portfolio Projects, which lists my best works. You can also click a category on the right side of the page to filter the posts.
If you’d like to get in touch, see the About page.
Thanks for visiting!
📌 1st and 2nd place in Two Coding Competitions
Our team won 1st and 2nd place in two coding competitions, Synergy Software Solutions 2024 and Blue Hacks 2024, respectively. We had developed a comprehensive personal carbon footprint calculator, as well as a mental health platform for students and guidance counselors.
Using a Neural Network to Classify Handwritten Digits
I compare the performance of a neural network to that of a K Nearest Neighbors model in classifying images of handwritten numbers. I also demonstrate the use of Grid Search for hyperparameter optimization.
Comparison of Regression Models for Predicting Bike Rentals
I compare the performance of three machine learning models (linear regression, decision tree, random forest) in predicting the number of bike rentals that may occur at a given time in Washington, D.C.
Using Linear Regression to Predict House Sale Prices
I interpret linear regression results to determine features that significantly affect house sale prices. I then use the same model to predict house prices, and evaluate the model using stratified k-fold cross-validation.
agriHanda: an Agricultural Disaster Risk Web App
I developed a web app that won two awards in the Project SPARTA PH Open Data Challenge for Butuan City.
Answering Business Questions for an Online Music Store using SQL
I use intermediate SQL techniques like views, joins, aggregations, and set operations in order to solve 4 scenarios about a hypothetical online music store. Results are communicated with Matplotlib and Altair visualizations.
How Student Demographic Affects SAT Performance in NYC
I combine multiple datasets about NYC high schools to explore the effect of students’ demographics on SAT performance. The project ends with a multiple linear regression model showing that the effect of race percentages on a school’s average SAT score is significant.
