In this section, we are going to explore each variable separately. We are going to summarize the data for each feature and analyze the pattern present in it. Univariate analysis is an analysis using individual features. We will also perform a bivariate analysis later in this section. Univariate analysis Now, let us do a univariate […]
In this section, we will derive the summary statistics for numerical columns. Before generating summary statistics, we will identify the categorical columns and numerical columns in the dataset. Then, we will calculate the summary statistics for all numerical columns. We will also calculate the mean value of each numerical column for the target class. Summary […]
Finally, you deploy your model into production and monitor for continuous improvement using ML Operations (MLOps). MLOps aims to streamline the process of taking ML models to production and maintaining and monitoring them. In this book, we will focus on data labeling. In a real-world project, the datasets that sources provide us with for analytics […]
In this step, you identify and gather potential data sources that may be relevant to your project’s objectives. This involves finding datasets, databases, APIs, or any other sources that may contain the data needed for your analysis and modeling. The goal of data discovery is to understand the landscape of available data and assess its […]
Controlling the state of a frontend application is one of the biggest challenges for a developer, as by nature, the interface is dynamic and needs to react to various user actions. Angular, with its stacks included philosophy, already had tools suitable for this task, and we studied in Chapters 5, Angular Services and the Singleton […]
