Handling Missing Data | Part 1 | Complete Case Analysis

Handling missing data is an essential step in the data preprocessing pipeline, ensuring that ML models are trained on high-quality, representative datasets, leading to more accurate and reliable predictions Techniques like imputation, dropping missing values, or advanced methods such as Multiple Imputation can be employed based on the nature and impact of missing data. Choosing the right strategy ensures the reliability and accuracy of your models. Code Used: https://github.com/campusx-official/1... ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn:   / campusx-official   CampusX on Instagram for daily tips:   / campusx.official   My LinkedIn:   / nitish-singh-03412789   Discord:   / discord   E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Intro 00:58 - Handling Missing Data 05:50 - Complete Case Analysis [CCA] 07:09 - Assumption for CCA 09:38 - Advantages and Disadvantages of CCA 11:39 - When to use CCA? 13:24 - Code Example