When should we Delete missing values in a given data set in Machine learning? Handling missing values is an important step in the preprocessing of data for machine learning models. The decision to delete missing values depends on the extent of missing data, the nature of the data, and the impact of missing values on the performance of your model. Here are some considerations: Percentage of Missing Values: If a small percentage of your data has missing values (e.g., less than 5%), you may choose to simply remove the rows with missing values, especially if the missing values are randomly distributed and not likely to introduce bias. If a large percentage of your data has missing values, removing those rows might lead to a significant loss of information. In such cases, other strategies, like imputation, might be more appropriate. Reason for Missing Values: Understanding why the values are missing can help in deciding the appropriate strategy. If values are missing completely at random, d...
Note: Please read previous article : Checking for Duplicate Values for better understanding. b. Identifying Missing Values in Dependent and Independent Variables Checking for missing values is a crucial step in the data analysis and preprocessing process for several important reasons: Data Quality Assurance: Identifying missing values helps ensure the quality and integrity of the dataset. It allows for a thorough examination of data completeness and accuracy. Avoiding Bias in Analysis: Missing values can introduce bias into statistical analyses and machine learning models. Detecting and addressing these gaps is essential to obtain accurate and unbiased results. Preventing Misleading Conclusions: Ignoring missing values may lead to incorrect conclusions and interpretations. It's important to be aware of the extent of missing data to avoid drawing misleading or inaccurate insights. Ensuring Validity of Results: Many statistical tests and analyses assume the availa...