A document by Utkarsh Mittal. Click on the document to view its contents.

Data science is an interdisciplinary subject that uses scientific methodologies, data mining techniques, machine-learning algorithms, and large amounts of data to extract information and insights. The article presents an overview of the current state and future possibilities of Data Science in a variety of sectors, explores the benefits, outlines the frameworks and methodologies employed, explains the current obstacles, and offers feasible solutions.

The recognition of data as a natural resource has made headlines in the new era of industrialization. Companies now leverage this resource to enhance their services and products, often promising optimal outcomes. While data has always held significant value, recent advancements in AI and ML frameworks have brought this fact to the forefront. However, it was not until major scandals involving large corporations came to light that the critical issue of privacy was remembered. Consequently, the intersection of data, AI and ML frameworks, and privacy has emerged as a new area of research. Although numerous works have reviewed the development of various data protection techniques, it seems that most of them address the subject from a single perspective or attribute the entire concept of data privacy to a specific technique. This review aims to present an overarching view of the topic. It offers a systematic guideline that establishes a proper connection among the three elements: data, AI & ML frameworks, and privacy. The paper delves into each element from both an abstract and concrete standpoint, presenting the latest techniques to address data privacy concerns, including numerous lab simulations. It also recommends tools and resources for further study. Ultimately, it wraps up the central topic by outlining the challenges and prospective future research directions. Keywords: Data Privacy; AI & ML; Differential Privacy; Federated Learning;

This study tackles the limitations of traditional distance metrics in machine learning, particularly their inadequacy in managing missing data and varying feature importance. We introduce a novel custom distance metric that offers a more nuanced and robust measure of similarity, addressing the complexities and imperfections of real-world data. Our extensive evaluation of the CustomKNN algorithm, leveraging this custom distance metric, reveals exceptional performance and robustness across diverse datasets, consistently outperforming traditional metrics in various classification tasks. The custom distance metric shows superior robustness to missing data and scalability, despite its higher computational complexity and memory requirements. Our findings highlight the versatility and effectiveness of the CustomKNN algorithm, establishing it as a formidable option for classification tasks. This research advances the development of more adaptable and context-aware distance metrics, significantly enhancing the accuracy and reliability of machine learning models in practical applications.

As conversational AI systems such as ChatGPT have become more advanced, researchers are exploring ways to use them in education. However, we need effective ways to evaluate these systems before allowing them to help teach students. This study proposes a detailed framework for testing conversational AI across three important criteria as follow. First, specialized benchmarks that measure skills include giving clear explanations, adapting to context during long dialogues, and maintaining a consistent teaching personality. Second, adaptive standards check whether the systems meet the ethical requirements of privacy, fairness, and transparency. These standards are regularly updated to match societal expectations. Lastly, evaluations were conducted from three perspectives: technical accuracy on test datasets, performance during simulations with groups of virtual students, and feedback from real students and teachers using the system. This framework provides a robust methodology for identifying strengths and weaknesses of conversational AI before its deployment in schools. It emphasizes assessments tailored to the critical qualities of dialogic intelligence, user-centric metrics capturing real-world impact, and ethical alignment through participatory design. Responsible innovation by AI assistants requires evidence that they can enhance accessible, engaging, and personalized education without disrupting teaching effectiveness or student agency.

Thе usе of unsupеrvisеd machinе lеarning tеchniquеs,  spеcifically K-mеans clustеring algorithms,  in urban studiеs has gainеd significant attеntion in rеcеnt yеars.  Thеsе tеchniquеs havе provеn to bе valuablе in analyzing and undеrstanding various aspеcts of urban dеsign,  such as land usе pattеrns,  transportation systеms,  and population distribution.  This articlе aims to providе a comprеhеnsivе rеviеw of thе application of K-mеans clustеring algorithms in urban studiеs. Thе findings of this rеviеw dеmonstratе thе widе rangе of applications of K-mеans clustеring in urban studiеs,  from idеntifying distinct land usе catеgoriеs to undеrstanding thе spatial distribution of social amеnitiеs.  Furthеrmorе,  it is rеvеalеd that thе usе of K-mеans clustеring in urban studiеs allows for thе idеntification and charactеrization of hiddеn pattеrns and similaritiеs among urban arеas that might not bе immеdiatеly apparеnt through traditional analysis mеthods.  Ovеrall,  thе usе of K-mеans clustеring algorithms providеs a valuablе tool for urban plannеrs and rеsеarchеrs in gaining insights and making informеd dеcisions in urban dеsign.  keywords: k-means clustering,unsupervised Machine Learning ,urban studies