Handling missing values in a dataset is a crucial step in machine learning. There are several approaches that can be used depending on the nature and amount of missing data. Here are some common techniques:

  1. Deletion: If the missing values are random and the amount of missing data is small, it may be reasonable to simply delete those rows or columns. However, this approach may result in loss of information, especially if the missing data is not random.

  2. Mean/Mode/Median Imputation: In this approach, missing values are replaced with the mean, mode, or median of the non-missing values in the same column. This method is simple to implement but may distort the original distribution of the data.

  3. Regression Imputation: If there is a correlation between the missing variable and other variables, regression can be used to predict missing values based on the available data. This approach assumes the missing values can be predicted accurately based on the relationship with other variables.

  4. Multiple Imputation: This technique involves creating multiple imputed datasets, where missing values are filled with plausible values. Each dataset is then analyzed separately, and the results are combined to obtain final estimates. Multiple imputation accounts for uncertainty related to missing values and provides more reliable estimates.

  5. K-nearest neighbors (KNN) Imputation: KNN imputation replaces missing values with the values from the nearest neighbors in the feature space. It calculates the distance between a missing data point and its k-nearest neighbors, and then imputes missing values using weighted averages or mode.

  6. Using Missingness Indicators: This approach involves creating additional binary variables to indicate whether a particular value is missing or not. This way, the model can learn the pattern and influence of missingness, and it can be particularly useful if the missingness itself carries valuable information.

  It is important to note that the choice of the handling method depends on the specific dataset and problem at hand. It is advisable to analyze the impact of the chosen imputation technique on the model's performance and consider the limitations and assumptions of each method.