Artificial IntelliTools

  Predicting whether an article will go viral is a puzzle that blends data science with human behavior. In this project, we worked with a large dataset of online news articles, aiming to forecast popularity (measured as the number of shares) using dozens of explanatory variables. The assignment was straightforward in its goal but complex in its execution: reduce dimensionality, train models, and report performance. Along the way, we uncovered lessons about interpretability, complexity, and the limits of linear regression in messy, real-world data. The Dimensionality Challenge Our dataset contained nearly 40,000 articles with 60+ explanatory variables  — ranging from keyword frequency to sentiment polarity. This posed the classic curse of dimensionality : too many features relative to the predictive signal often leads to overfitting, inefficiency, and inscrutable models. To tackle this, we explored three modeling paths: Full features  — a baseline model with all predictors. Feature...

  Speech by Emmanuel Kasigazi, KASA President Introduction Good morning, everyone. My name is Emmanuel Kasigazi, and I’m honored to serve as your President of the Katz School African Student Association — KASA. I’m here studying Data Analytics and Visualization, originally from Uganda with an undergraduate degree in Information Systems. But more importantly, I’m here as someone who understands your journey. I’m a seasoned engineer and entrepreneur, and just like you, I’m an immigrant. I arrived here last year. Before that, I spent time in Toronto, and years ago, I lived in South Sudan. I know what it’s like to not live in your home country. I understand what you might be going through, what you might be experiencing. Different countries, yes, but leaving home is leaving home — and that experience connects us all. My Background Back home, I’ve been a leader throughout my life. I ran companies with teams of employees, worked across various sectors from tech to branding, printing prod...

  Why This Matters When people hear “venture capital,” they picture boardrooms full of billionaires, complex financial models, and Silicon Valley jargon. But the deeper I’ve gone into this world—through programs like Venture Institute, NSF I-Corps, and my own entrepreneurial journey—I’ve learned a simple truth: venture capital is still business. Like running a chapati stand in high school or lending to small SMEs in East Africa, it comes down to the same loop: Get resources. Add value. Deliver results. Grow trust and relationships. The magnitude changes, but the fundamentals don’t. The People Game One of my biggest insights from the Venture Institute is that venture is a people’s game. Limited Partners (LPs) are the customers of a fund, not startups. GPs live and die by relationships—just as I once did when one client made up 50% of my branding business, and we nearly collapsed when they pulled out. In VC, no LP should ever hold more than 20% of a fund. Diversification isn’t just...