Why Multicollinearity is Bad? What is Multicollinearity? How to detect and remove Multicollinearity

Multicollinearity is a phenomenon where two or more independent variables are highly correlated. In other words, one predictor variable can be used to predict the value of another. This creates redundant information, skewing the results in a regression model. ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn:   / campusx-official   CampusX on Instagram for daily tips:   / campusx.official   My LinkedIn:   / nitish-singh-03412789   Discord:   / discord   👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! 💭Share your thoughts, experiences, or questions in the comments below. I love hearing from you! ⌚Time Stamps⌚ 0:00 - 3:12 - What is Multicollinearity 3:13 - 10:30 - Why Multicollinearity is Bad? 10:31 - 12:14 - Types of Multicollinearity 12:15 - 16:15 - How to detect and remove 16:16 - 17:05 - Does it affect all ML algorithms?