Lecture 56: Machine Learning: Logistic Regression on challenging data part02

n this video, we apply Logistic Regression to the Titanic dataset, one of the most popular and challenging datasets for classification tasks. We’ll walk through the process of preprocessing this real-world data, which involves dealing with missing values, categorical variables, and class imbalances. You’ll learn how to implement Logistic Regression in Python using scikit-learn, and we’ll discuss techniques to improve model performance on this challenging dataset. By the end of the tutorial, you’ll know how to handle tricky data issues and apply Logistic Regression to solve complex classification problems, using Titanic as an example. This is the part02 of the video. It has three parts. The first part is in Lecture 55 and third in Lecture 57 The link for folder containing all the Logistic Regression codes and datasets can be found in the comments Link for next video:    • Lecture 57: Machine Learning: Logistic Reg...