Regression quantifies relationships — how much does changing X move Y? Today you build linear and logistic regression models, interpret coefficients correctly, validate with held-out data, and understand where regression fails.
By the end of this lesson you will fit a linear regression model with scikit-learn, interpret slope and intercept in context, evaluate model fit with R-squared and residual plots, fit a logistic regression for binary classification, and identify signs of overfitting.
linear regression is the foundation of Day 4. Every concept that follows builds on the mental model you establish here. The most effective approach is to understand the principle first, then apply it — skipping straight to implementation creates gaps that compound into confusion later.
Work through each example in this lesson sequentially. The concepts connect, and the order is deliberate. If something is unclear, slow down at that point rather than pushing past it — a ten-minute pause now saves hours of debugging later.
Understanding linear regression requires seeing it in motion. The code below is not a complete application — it is a minimal, working illustration of the key mechanism. Study the pattern, run it, break it deliberately, then fix it. That cycle builds real comprehension.
Once the basic pattern works, the logical next step is logistic regression. This is where the abstraction becomes useful — you move from understanding the mechanism to applying it to real problems. The transition is usually smaller than it feels. Most of the hard work happened in Section 1.
R-squared completes today's picture. It is where linear regression and logistic regression converge into a pattern you can apply to novel problems. This integration step is often where the day's learning consolidates — if the earlier sections felt abstract, this one typically makes them click.
Implementing linear regression alone handles the happy path. Real systems encounter edge cases, invalid input, and unexpected state. Missing logistic regression means missing those guards.
Combining linear regression with logistic regression gives you a complete, defensible implementation. The extra lines cost ten minutes; the robustness they add is worth hours of debugging time.
Several mistakes appear consistently when engineers encounter Regression Analysis — Finding Relationships in Data for the first time. Recognizing them now costs nothing; encountering them in production costs hours.
Two intensive days (Thu–Fri) with an instructor who has taught thousands of engineers. Cohorts in 5 cities, June–June–October 2026 (Thu–Fri).
Reserve Your Seat — $1,490Before moving on, you should be able to answer these without looking: