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

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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?

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