Missing values are harmful to machine learning models! not sure how many % is true on this..

1. 1Identify missing values using data visualization tools
2. 2Apply imputation techniques to fill missing values
3. 3Test the impact of different imputation methods on model accuracy
4. 4Select the most effective imputation method for the dataset
5. 5Implement the chosen imputation method in the data preprocessing pipeline

Data scientists and machine learning engineers benefit from handling missing values as it directly impacts model performance and reliability

💡 Ignoring missing values can lead to biased or inaccurate models, while proper handling can significantly improve performance

💡 Handling missing values can boost machine learning model accuracy!