## 1 Introduction  
As globalization continues, an ever-increasing trend of immigration and cultural exchange is observed. Upon arriving in another country, immigrants have to overcome language barriers, along with many other challenges. The degree to which a reader finds a document difficult to understand is measured through readability assessment, a process supported by machine learning and linguistics. The automation of this process allows us to provide appropriate reading material that readers can understand regardless of their language proficiency while keeping the core information intact. When selecting reading material, the degree of difficulty in understanding is crucial. If the content is too sophisticated, the reader might give up on acquiring information or, even worse, be misinformed. This results in wasted valuable time and effort, especially in times of need. Text readability assessment is an important part of text simplification. As such, machine learning (ML) techniques have been widely employed to offer solutions to the above problems, and they have been used in numerous Natural Language Processing (NLP) tasks, such as fake news detection [1], text categorization [2], translation evaluation [3], etc. These techniques are being used more and more in today's technologies as we attempt to classify essays, paragraphs, or even sentences by levels of complexity. Through this classification, we can retrieve information at an appropriate complexity level for the target reader, ensuring that they neither find the text too difficult to comprehend nor too easy to maintain focus. By knowing the profile of the user, content classified with a readability score can assist a plethora of technologies like web search engines, as well as other fields such as e-learning platforms, news reporting, online libraries, legal documentation, and many more. In a wide variety of previous papers, the models tended to focus on specific target demographics. The current work utilizes the Wikipedia dataset, which consists of sentences from this online encyclopedia that are made for everyone and are freely accessible to a wide audience. However, as mentioned in the previous paragraph, static content cannot satisfy everyone's needs. In this paper, the focus is on binary readability assessment of sentences rather than paragraphs or whole texts. We begin by reviewing the results, effective methods, and features used in influential papers in the literature. Subsequently, we introduce our own feature sets, features, methods, and approaches to readability assessment of the selected corpus. Performing this task at the sentence level has numerous practical applications, including improving educational materials by ensuring that each sentence is accessible to students with varying reading levels; simplifying technical documentation, especially when the text volume is small; and refining legal documents to eliminate ambiguity. Other examples include customer support scripts for clear and effective troubleshooting, or even in healthcare, where clear and concise sentences in patient information leaflets and consent forms are essential. These applications demonstrate the importance of sentence-level readability in ensuring effective communication across diverse contexts. Compared to paragraph-level or whole-text assessments, sentence-level assessments allow us to acquire feedback on the overall organization, flow, and general complexity of paragraphs while obtaining specific results, which is essential for precise revisions and improvements. L2 reader-related real-life applications might include language learning apps, customized educational content, and adaptive testing platforms. This paper evaluates the effectiveness of shallow features, particularly in texts designed to aid L2 readers' comprehension. Due to computational constraints, tool limitations, and in line with our hypothesis, our feature set primarily comprises shallow features.