For this project, I’m analyzing publicly reported tech layoffs from 2020 to 2024 to see if companies naturally group together based on how and when they reduced staff. My goal is to find patterns among company size, funding stage, industry,…
Association rules are used to find connections between items in big sets of data. In simple terms, they help show what things usually go together. One common use is in market-basket analysis, where stores look at what customers often buy…
I’m using the Tech Layoffs Dataset (2020–2024) from Kaggle, which tracks tech layoffs collected from public reports and websites. It includes company names, industries, locations, number of employees laid off, total company size, percentage of workforce cut, and the dates…
Clustering is a machine learning technique used to group similar data points together based on their shared characteristics. It’s an example of unsupervised learning because it finds patterns in data without using labels. In simple terms, clustering helps organize messy…
The goal of this project was to answer one main question: “What features most strongly influence a home’s sale price, and how accurately can we predict that price using linear regression?” To explore this, I used the housing dataset from…
Linear Regression is a way to draw a straight line through data points so you can predict something that’s a number. Let’s say you want to guess someone’s weight based on their height. You plot out a bunch of people’s…
For this project, I wanted to answer a question I’ve always been curious about: how do email providers or phones actually know when a message is spam? I get random texts and emails that look sketchy, and somehow Gmail or…
High-Level Overview of the Algorithms 1. Naive Bayes This is based on Bayes Theorem that has a “naive” assumption where features are independent. These tend to be much faster and works great for text classification problems like spam detected (using…
What are Decision Trees? Decision Trees are a type of supervised machine learning model used for both classification and regression problems. They work by splitting the dataset into smaller and smaller groups based on feature values. At each “node,” the…
What is machine learning? Teaching a computer to find patterns in data so it can make predictions without being explicitly coded for every case. What is classification? Predicting a category/label (spam vs. not spam, churn vs. no churn, etc.). Steps…
