Free 100 Days of Data Science Master Classes - 💫 Machine Learning for Data Science - 16: Iterative Imputer | MICE | MCAR | MAR | MNAR by @theiScale

🚀 Welcome to Day 16 of our Machine Learning series with @theiScale GitHub Link for Day 16 Notes: https://github.com/TheiScale/30_Days_Machine_Learning/tree/main/Day16 ✨ Kickstart your career as a Data Analyst. Apply today! - https://www.theiscale.com/DataAnalytic?analytics=13 ❇️ Explore our Job Oriented Courses: https://www.theiscale.com/explore-course ➖➖➖➖➖➖ 📱 For Any Further Queries or Doubts? Contact- 7880-113-112 (Student Helpline Number) For any query connect in WhatsApp with us: https://wa.me/917880113112 ➖➖➖➖➖➖ ✳️ Join Telegram Channel- https://t.me/TheiScale ✳️ Join WhatsApp Channel- https://whatsapp.com/channel/0029VaB5ekEKQuJQV572vi2c ➖➖➖➖➖➖➖ 🔗 Download App Google Play: https://play.google.com/store/apps/details?id=com.logixhunt.ihhpet&pli=1 Welcome to Day 16 of "30 Days of Machine Learning for Data Science" presented by The iScale! In today's session, we delve into Iterative Imputer, MICE (Multiple Imputation by Chained Equations), and the crucial concepts surrounding missing data patterns: MCAR, MAR, and MNAR. Here's what you'll explore: • Iterative Imputer: Understand the principles and applications of Iterative Imputer, a powerful technique for imputing missing data through iterative estimation. • MICE (Multiple Imputation by Chained Equations): Learn about MICE, a popular imputation method that generates multiple imputed datasets to handle missing data effectively. • Missing Completely at Random (MCAR): Explore the concept of MCAR, where missingness is unrelated to observed or unobserved data. • Missing at Random (MAR): Understand MAR, where the probability of missing data depends only on observed variables. • Missing Not at Random (MNAR): Delve into MNAR, where the probability of missing data depends on unobserved variables, leading to biased estimates. Finding Predictive Value for Iterative Imputer Technique: Discover methods for determining predictive value and optimizing the Iterative Imputer technique for enhanced imputation accuracy. Join us as we unravel the complexities of missing data patterns and introduce powerful imputation techniques to address them effectively. Enhance your understanding of Iterative Imputer, MICE, and the underlying principles of MCAR, MAR, and MNAR. Watch now to empower your machine learning journey and elevate your data science expertise with The iScale! Don't forget to like, share, and subscribe for more insightful content from @theiScale #theiscale #iscale #datascience #dataanalytics #machinelearning #machinelearningbasics #machinelearningfullcourse #viral #career #job #coding #programming #code #python #ai #artificialintelligence #ds #career #codewithus #freshers