Missing values are the values that are absent from the data. Absent values can occur due to human error, privacy concerns, or the value not being filled in by the respondent filling in the survey. This is the most common problem in data science and the first step of data preprocessing. Missing values affect a machine learning model's performance. Missing values can be handled in the following ways:
- Drop the missing value records.
- Fill in the missing value manually.
- Fill in the missing values using the measures of central tendency, such as mean, median, and mode. The mean is used to impute the numeric feature, the median is used to impute the ordinal feature, and the mode or highest occurring value is used to impute the categorical feature.
- Fill in the most probable value using machine learning models such as regression, decision trees, KNNs.
It is important to understand that in some cases, missing values will not impact the data. For example, driving license...
