Harmful Brain Activity Decision Trees (DTs) : What is a decision tree? A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes.

exploratory Data Analysis (EDA): is crucial for developing effective machine learning models. This article discusses the various techniques and methods used in EDA, such as scatter plots, histograms, box plots, and descriptive statistics, to identify trends and patterns in datasets. It also explains how to handle missing values and transform categorical and numerical data for optimal data preparation.

Additionally, the article covers the process of model selection and optimization, including hyperparameter tuning using GridSearchCV, evaluating model performance using metrics like accuracy, classification report, confusion matrix, and ROC-AUC score, and cross-validation. By following these steps, readers can create accurate and effective machine learning solutions.

Why is it important to perform EDA? Methods and techniques of EDA: There are several techniques and methods for performing an EDA, such as scatter plots, histograms, box plots, and descriptive statistics. The choice of techniques depends on the nature of the data and the goal of the analysis.

Data visualization: Data visualization is a powerful tool for identifying trends and patterns in datasets. Charts such as lines, bars, scatter, and box plots facilitate the identification of relationships between variables, frequency distributions, and the presence of outliers.