6 Different Ways to Compensate for Missing Data (Data Imputation with examples)

Many real-world datasets may contain missing values for various reasons. They are often encoded as NaNs, blanks or any other placeholders. Training a model with a dataset that has a lot of missing values can drastically impact the machine learning model's quality. Some algorithms such as scikit-learn estimators assume that all values are numerical and have and hold meaningful value. One way to handle this problem is to get rid of the observations that have missing data. However, you will risk losing data points with valuable information.