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Analyzing Congressional Floor Debates with LDA Topic Modeling

Topic modeling is a powerful technique in natural language processing (NLP) that helps us discover the underlying themes or topics within a collection of texts. In this blog post, we will explore the application of Latent Dirichlet Allocation (LDA) topic modeling to analyze the floor debates of the 110th Congress, focusing exclusively on the House of Representatives. The dataset is divided into subfolders, with "m" representing male speakers, "f" for female speakers, "d" for Democrats, and "r" for Republicans. Let's dive into the process of topic modeling and uncover the main themes of these congressional debates.

Evidence-Based Investment Using Monte Carlo Simulations

In the dynamic world of finance, making informed investment decisions can be quite challenging. Fortunately, advancements in technology and mathematical modeling have given rise to powerful tools that enable investors to gain a deeper understanding of potential outcomes. One such tool is the Monte Carlo Simulation, a technique that harnesses the power of randomness to forecast various scenarios and make evidence-based investment choices. In this article, we'll delve into the realm of evidence-based investing using a Monte Carlo Simulation, building upon the insightful notebook developed by Matt Macarty.

Embracing Monorepos for Microservices: Advantages, Challenges, and Best Practices

In modern software development, the choice between monorepos and polyrepos (multiple repositories) has become a pivotal decision for teams, particularly those working with microservices. Monorepos, a single repository housing all application and microservice code, offer an array of benefits when combined with streamlined build and deploy pipelines. In this article, we explore the advantages, misconceptions, challenges, and best practices of monorepos to help you determine whether this approach is the right fit for your team's development journey.

Unveiling the Power of Landsat Atmospheric Correction Using R

In the realm of remote sensing and geospatial analysis, Landsat imagery plays a pivotal role in understanding and monitoring Earth's surface. However, to extract accurate information from these images, it's crucial to apply proper atmospheric correction techniques. In this blog post, we'll dive into the process of atmospheric correction using the R programming language and the RStoolbox package. Best of all, this package is both free and open source, making it accessible to all.