One of the most critical issues in developing the model to analyze real-world problems is the input variable selection problem. Determining how many input variables the model needs to predict the target variable and gain insight into the research question is one of the most important considerations when developing a model, especially for an Edge-AI model. This also relates to justifying the sample size to maximize the research’s resources and efficiently test the hypothesis. Thus, the feature selection techniques will be presented as the core of this chapter. Feature selection and input variable selection are used interchangeably. Both refer to the process of choosing a subset of relevant variables (features) from the dataset that will be used to train the model. Both goals usually improve model performance, reduce overfitting, and simplify the model. It is a crucial step in the model development process to assess the significance of each feature in relation to the target variable, ensuring that selected features contribute meaningfully to the model. It facilitates a clearer understanding of the relationships between specific features and the target variable. By selecting only the most relevant features, the model becomes more focused and can lead to a more accurate and efficient model, improving the predictive performance on training and unseen data. Also, feature selection helps prevent overfitting, where a model becomes too complex and fits the noise in the training data, leading to poor generalization on new data.