Handling missing values in a training set is an important step in data preprocessing. There are several techniques that can be used, depending on the nature of the data and the specific problem at hand. Here are some common techniques:

  1. Deletion: This involves removing observations or variables with missing values. If the number of missing values is very small compared to the total dataset, deleting those observations may not significantly affect the overall analysis. However, if missing values are prevalent, this approach may lead to loss of valuable information.

  2. Imputation: Imputation is the process of estimating missing values based on the known values in the dataset. There are various imputation techniques available, including:

   a. Mean/median imputation: Replacing missing values with the mean or median of the available data for that variable. This method is simple and easy to implement but may oversimplify the data.

   b. Mode imputation: Imputing missing categorical values with the mode (most frequent value) of that variable.

   c. Regression imputation: Using regression models to predict missing values based on the relationship with other variables.

   d. K-nearest neighbor imputation: Estimating missing values by averaging the values of the nearest neighbors (based on other variables) of the observation with the missing value.

   e. Multiple imputation: Creating multiple imputed datasets by imputing missing values multiple times using a chosen method. This accounts for the uncertainty in the imputation process.

  3. Indicator variable: Creating an additional binary variable that indicates whether a value is missing or not. This allows the missingness to be explicitly included in the analysis.

  4. Expert knowledge: In some cases, domain experts may have insights to deal with missing values. Their knowledge can help make informed decisions on how to handle missing values specific to the problem domain.

  It is important to note that the choice of technique for handling missing values depends on the data distribution, the amount and nature of missingness, and the specific analysis goals. It is advisable to carefully evaluate the impact of missing values and assess the suitability of different techniques before making a decision.