For my Master’s in Artificial Intelligence & Cloud Computing (Integrated Track) I am preparing a research paper that hinges on solid, defensible data analysis. The dataset I need to work with is entirely unstructured—think raw text, logs, or similar free-form inputs—and I must turn that material into clear, publishable insights for the results section of the paper. The goal is twofold: • Transform and clean the unstructured data so it is ready for modeling (Python, pandas, spaCy or similar libraries are all acceptable). • Run and document the analysis itself—statistical exploration, model selection and evaluation—so the findings can drop straight into an academic manuscript with proper figures, tables and code references. Deliverables I will need from you: 1. Well-commented code (Jupyter Notebook or Python script) that ingests the raw data, performs preprocessing, and executes the chosen models. 2. Intermediate and final visualizations or metrics that support the discussion in my paper. 3. A concise methods write-up (roughly 1–2 pages) outlining algorithms, parameter choices, and cloud resources used—ready to slot into the paper’s methodology section. Cloud deployment is optional, but if leveraging AWS, Azure, or GCP can shorten runtimes or demonstrate scalability, feel free to propose it; I have student credits available on each. Accuracy, reproducibility, and clear documentation are critical. If you have expertise in NLP, transformer models, or handling large-scale unstructured datasets, your approach will fit perfectly with my research objectives.