ML Cost-Benefit Thesis Writing -- 2

I need a complete master-level thesis that investigates the cost-benefit of applying machine-learning-based quality control in manufacturing. The study must address three research questions:

• RQ1 – Performance: compare a decision tree, random forest and artificial neural network by building models, generating confusion matrices and reporting accuracy, precision, recall and F1.
• RQ2 – Economics: translate false-negative and false-positive rates into monetary terms, add implementation costs and show the net economic value of each model against an explicit baseline.
• RQ3 – Break-even: determine the minimum improvement in prediction quality that makes machine learning economically viable and explore how that threshold shifts under alternative cost scenarios.

Your final output is a polished written report that meets university formatting standards, incorporates at least 90 peer-reviewed citations, and weaves the technical and economic analyses into a coherent narrative. Please include all figures, tables, equations and appendices necessary for replication. Code, datasets or slide decks are welcome if they help, but they are not compulsory—what matters is that the written thesis is rigorous, clearly argued and ready for academic submission.

I am flexible on the choice of tools: Scikit-learn, TensorFlow, PyTorch or any other library is fine so long as results are reproducible and all steps are documented in the methodology chapter.

Deliverables
1. Full thesis document (Word or LaTeX + PDF) with >90 citations in APA or IEEE style
2. Reference list, data-collection description and economic calculation sheets

Acceptance criteria: the thesis must directly answer RQ1–RQ3, pass standard plagiarism checks, and present statistically sound results that support the economic conclusions.