Detail three techniques for handling missing data, explaining the advantages and disadvantages of each with appropriate use cases.

Handling missing data is a crucial step in data preprocessing as missing values can significantly impact the performance and reliability of data analysis and machine learning models. Here are three common techniques for handling missing data, along with their advantages, disadvantages, and appropriate use cases: 1. Deletion (or Removal): This method involves either removing rows or columns containing missing data. In row-wise deletion, any data record (row) that has one or more missing values is completely removed from the dataset. In column-wise deletion, an entire feature (column) is removed if it contains a significant number of missing values. *Advantages:Row-wise deletion is straightforward to implement and can be suitable when the proportion of missing values is relatively small, and you are not losing significant amounts of valuable data. Column-wise deletion can be a good choice if a particular feature has a very large number of missing entries, and not removing it would hinder analysis. These methods also avoid introducing any bias from imputation if handled correctly. *Disadvantages:The main disadvantage is the potential for significant data loss. Removing rows or columns can lead to the loss of valuable information and might reduce the representativeness of the sample, especially if the missingness is not completely random. For example, if a survey has a question about income and people with lower income levels are more likely not to respond, deleting those responses will bias the sample towards higher incomes. This is also a problem with column deletion when a useful feature is entirely removed just because it has missing data points. Additionally, when the proportion of missing data is large, deletion can lead to a dataset that is too small to be effectively analyzed or used for training a machine learning model. *Appropriate Use Cases:Row deletion can be used when a very small number of rows have missing values, and those values are randomly missing with minimal impact on the analysis or model performance. Column deletion is suitable when a feature ha....