2025-09-21 –, Space 2
In this talk, we’ll explore how to build, train, and evaluate regression and classification models using Python on real-world datasets. From predicting house prices to classifying customer churn, this session will provide practical insights into combining these techniques for impactful data science applications.
We’ll start with core concepts and the difference between regression and classification. Then, we’ll understand the fundamentals of preparing data, building models, and evaluating them using intuitive metrics. You’ll see how to approach both types of problems and understand when to use which.
- Understanding regression vs. classification and choosing the right model
- Loading and cleaning datasets using Pandas
- Understanding regression and classification models using Scikit-learn
- Evaluating performance with metrics like RMSE, accuracy, precision, and recall
- Avoiding overfitting and improving generalization with regularization and cross-validation
Basic
I'm a Software Development Engineer II at Adobe with a deep passion for building impactful software and exploring the boundaries of what Python can do. My work spans systems design, automation, AI, and hardcore development. Outside of my day job, I enjoy experimenting with creative tech projects, speaking at events, and helping others learn through hands-on, practical sessions. I believe in making technology approachable, useful, and—whenever possible—a little fun.