What are missing values?

Missing values refer to the absence of data for certain observations or variables in a dataset. These missing values can occur for various reasons, ranging from errors during data collection to intentional omissions. We need to handle them very carefully to achieve an accurate predictive model. Commonly missing values are represented by two ways in datasets which are discussed below–>

• NaN (Not a Number): In numeric datasets, NaN is often used to represent missing or undefined values. NaN is a special floating-point value defined by the IEEE standard which is commonly used in programming languages like Python and libraries like NumPy.

• NULL or NA: In database systems or statistical software, NULL or NA may be used to denote missing values. These are only placeholders which signify the absence of data for a particular observation.

Data is the cornerstone of any analytical or machine-learning endeavor. However, real-world datasets are not perfect yet and they often contain missing values which can lead to error in the training phase of any algorithm. Handling missing values is crucial because they can lead to biased or inaccurate results in data analyses and machine learning models. Strategies for dealing with missing values include imputation (replacing missing values with estimated or calculated values), removal of incomplete records, or the use of advanced techniques like multiple imputation. Addressing missing values is an essential aspect of data cleaning and preparation to ensure robust and reliable analyses. In this article, we will discuss how to handle missing values with the CatBoost model.