100 Days of Machine Learning | CampusX - Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2

The Z-score method identifies outliers by measuring how far each data point is from the mean in terms of standard deviations. Points beyond a certain Z-score threshold are considered outliers. By applying this method, we can effectively detect and remove outliers, enhancing the robustness of our data analysis. Code Used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day42-outlier-removal-using-zscore ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Intro 00:30 - Outlier removal using Z score 03:40 - Z Score Formula 05:07 - Outlier Treatment 08:00 - Code Demo 14:08 - Capping