Handling missing data is a crucial part of the data preprocessing stage in any data science or machine learning project. Missing values can distort analysis, reduce model accuracy, and lead to biased results if not treated properly. Fortunately, several imputation techniques are available to manage this issue effectively. If you want to learn these methods in depth, consider joining the Data Analyst Course in Mumbai at FITA Academy. It’s a great way to build your skills and confidence in handl...