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 […]
The Angular framework is continually evolving with new features and optimizations, but to help communities and developers keep organized and their applications up to date, the Angular team uses semantic versioning to number their releases. A semantic version number is composed of three parts and each part has the following representation: In this book, we […]
With the Docker image we created, we can run our project on any cloud provider that offers container services. However, there are other ways to deploy our Angular project. One of these alternatives is Azure Static Web Apps, a service that specializes in web page design and allows automatic integration with GitHub. Let’s see it […]
After finishing the task of deploying our backend, we need to change our frontend project to make requests to our cloud infrastructure. But here, a problem arises. We want to access our published backend when we are in production, but the team needs to continue accessing the API locally to develop new features in a […]
Before preparing our gym diary project for production, let’s first upload the backend to a cloud service so that our page has access to the data. We chose the Azure service for this book, but the concepts in this chapter can also be applied to other cloud services, such as AWS (https://aws.amazon.com) and GCP (https://cloud.google.com). […]
