Prompt Design Regression Study

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@glanegonsalves

Hey there Glane here, I can design the full Python-based experimental workflow focusing specifically on Data Preprocessing and Evaluation Metrics for in-context polynomial regression. I’ll curate or generate a reproducible nonlinear dataset, implement controlled preprocessing, feature engineering, and train/validation/test splits, then create systematically varied prompt templates and evaluate them using MSE, MAE, R², plus useful diagnostic measures. I’ll run the experiments across at least two transformer-based language models, using fixed seeds and a consistent experimental protocol so results are reproducible, and provide a well-documented Jupyter Notebook/Markdown report, plots, comparison tables, README, and interpretation that can later be extended to the remaining experimental axes.

@mayankinnovative

Drawing from over 20 years of domain expertise in PHP and Python-based development, your project aligns perfectly with the analytical, systematic, and ML-driven approach that I bring to my work. I have extensive knowledge of data science and machine learning which will be invaluable for this prompt design regression study. My solid skills in Python and libraries such as PyTorch can be counted on for clean data preprocessing and developing an evaluation pipeline that incorporates diverse regression metrics. Moreover, my experience in building scalable solutions will ensure that your project remains extensible to future research. I recognize the importance of clear documentation for reproducibility, and my Jupyter notebooks are always structured to ensure comprehensive understanding of the code-base and methodology. This paired with my knack for delivering concise technical reports makes me the right person for distilling complex information into actionable insights. Finally, my commitment to long-term support means that even after delivery, I'll be available for any queries or assistance you may need in the future. Choose me not just for this phase but as a reliable partner who can undertake subsequent axes too. Let's connect and discuss how we can take this project to new heights of discover

@HanyasSalem

Dear Project Lead, Thank you for outlining this detailed and compelling investigation. The intersection of prompt engineering and regression performance is a critical frontier in LLM evaluation, and your focus on Data Preprocessing and Evaluation Metrics as foundational axes is a rigorous starting point. I am confident I can deliver the robust, reproducible pipeline and insightful analysis you require. My approach is grounded in controlled experimentation, statistical rigor, and a modular codebase designed to seamlessly accommodate your remaining four axes (e.g., Model Size, Sampling Strategy, Chain-of-Thought, and Output Formatting) in the future.

@Bhartifreelancer

Hi, I can handle this as a reproducible prompt-engineering experiment, with the work tightly focused on the two requested axes: Data Preprocessing and Evaluation Metrics. I’ll be careful to avoid confounding the two focal axes with unnecessary changes to the other four dimensions, so the experiment can be extended later without redesigning the entire pipeline. Most importantly, I’ll document the methodology and assumptions clearly so every reported result can be reproduced and the conclusions are supported by the actual experiments not just qualitative observations. I’d be happy to review your existing experimental framework, if available, and align the implementation with it before starting. Best regards, Bharti

@albertc1996

I’d structure this as a reproducible experimental pipeline rather than optimizing for one-off model results. Approach: Define the dataset generation and preprocessing procedure first, including normalization, feature representation, train/validation/test splits, and fixed seeds Create controlled prompt templates where the preprocessing and evaluation variables can be changed independently, so the experiment does not accidentally mix multiple factors Build a reproducible evaluation pipeline reporting MSE, MAE, R², and additional diagnostics that help explain model behaviour rather than relying on a single score Run the same experiment across at least two transformer-based models with identical evaluation conditions Track every experiment configuration, prompt version, model version, seed, and metric so results can be reproduced exactly Present the results in a Jupyter notebook with tables, plots, statistical comparisons, and a concise interpretation of the observed trends Keep the experiment structure modular so the remaining four research axes can be added later without rebuilding the pipeline 7 days for the reproducible dataset, experiment pipeline, multi-model evaluation, notebook, and initial technical report. Best regards, Albert

@alluankit01

Hi, I can support your prompt design regression study by focusing on the Data Preprocessing and Evaluation Metrics axes while keeping the experiment structure reusable for the remaining axes later. The best solution is to first create or curate a clean polynomial regression dataset, define preprocessing steps, prepare controlled prompt templates, and then run reproducible experiments across two transformer-based models. I’ll structure the pipeline so normalization, tokenization, data splits, feature representation, metrics, prompt variants, and seed values are clearly documented. I’m comfortable with Python, PyTorch/JAX-style ML workflows, regression analysis, prompt engineering, transformer model evaluation, dataset preprocessing, MSE/MAE/R-squared reporting, statistical comparison, plots, experiment tracking, and technical report writing. Deliverables will include: * Clean polynomial regression dataset * Documented preprocessing pipeline * Prompt template variations * Train/validation/test split setup * Evaluation pipeline * MSE, MAE, and R-squared metrics * Prompt-specific diagnostics * Cross-model comparison * Results tables and plots * Jupyter/Markdown technical report * README with reproducible steps I’ll focus on making the study reproducible, clearly interpreted, and useful for extending the same framework to the other prompt-design axes later. Best regards Ankit

@Marwan328

Hello, I’m a Machine Learning Engineer with experience in Python, regression, model evaluation, Pandas, Scikit-learn, PyTorch, and NLP workflows. Your experiment is particularly interesting because it requires controlled prompt variations and reproducible evaluation rather than simply testing which prompt “looks better.” I can build the polynomial-regression dataset and preprocessing pipeline, define controlled prompt templates, and evaluate the results using MSE, MAE, and R² along with additional diagnostics where useful. I’ll structure the experiments so preprocessing choices and evaluation metrics remain clearly separated, making the methodology easy to extend to the other four axes later. I can also run the experiments across two transformer-based models and provide reproducible seeds, clean Python code, results tables, plots, and a technical Jupyter/Markdown report. The final workflow will be reproducible on your machine with clear setup instructions and documented preprocessing decisions. I’m available to start immediately and can work within your ₹400/hour budget.

@InjectAI

I can build this systematic prompt-engineering and polynomial-regression evaluation study with a strong focus on Data Preprocessing and Evaluation Metrics, while keeping the framework extensible to the remaining axes. I have hands-on experience with Python, Pandas, NumPy, machine learning, regression, data preprocessing, model evaluation, and AI/LLM workflows. I can create a controlled polynomial-regression dataset, document normalization, feature engineering, tokenization, and reproducible train/validation/test splits. I will develop controlled prompt templates, an automated evaluation pipeline for MSE, MAE, R² and additional diagnostics, and run experiments across two transformer-based models. Results will include tables, residual/error plots, cross-model comparisons, and clear interpretation of how preprocessing and metric choices influence performance. Deliverables will include clean Python/PyTorch code, fixed seeds, README, Jupyter Notebook/Markdown report, experiment results, and a modular structure that allows the other research axes to be added later. I can take this from experimental design through reproducible results and technical reporting. Please see my profile for my previous AI, computer vision, Python, and ML work and relevant project experience. I’m ready to start with the baseline dataset and experimental framework.

@rakhshanz3

With my extensive background in Artificial Intelligence and Machine Learning combined with nearly 20 years of experience, I am well-suited to tackle this comprehensive Regression Study project you have in mind. Not only do I have a solid foundation in teaching and research from my days as a lecturer but I have also demonstrated my mastery of these skills through my work with multinational software companies and as Chief Technology Officer of an AI start-up. Drawing on my deep understanding of regression tasks and my expertise in data preprocessing, I assure you that you will receive a clean, well-documented dataset carefully curated or generated for polynomial regression. The preprocessing steps including normalization, tokenization strategy, train/validation/test partitioning, and any feature engineering aspects will be outlined thoroughly for your review. My fluency in Python, PyTorch or JAX ensures that you will receive reproducible code with clear README - this is non-negotiable.

@cyberpower65

As a seasoned full stack developer with substantial experience in Machine Learning, I would be thrilled to bring my expertise to your Prompt Design Regression Study. Transforming complex business ideas into reliable software is my forte, and I'm no stranger to implementing intricate algorithms like polynomial regression. Your project entails the curation of a clean dataset, preprocessing steps, precise train/validation/test partitioning, and designing systematic prompt templates. I've got all your needs covered! My proficiency in Python, PyTorch, and JAX combined with my love for well-documented code will ensure reproducibility on your machine.

@samuelf2905

I can build the experiment around your two focus areas—data preprocessing and evaluation metrics—while keeping the structure extensible for the other four axes. I’ll create reproducible polynomial-regression data, implement controlled prompt variations, evaluate with MSE, MAE, and R², and add useful diagnostics for comparing prompt behaviour. I’ll also run the experiments across two transformer-based models, with fixed seeds and a clean Python/Jupyter workflow so the results can be reproduced on your machine. You’ll receive: Reproducible Python code + README Jupyter notebook/technical report Experiment results and comparison tables Regression plots and diagnostics Clear methodology and interpretation I have experience building AI-powered systems, data-processing workflows, and LLM-based solutions, and you can see my relevant work here: Portfolio: https://www.freelancer.com/u/samuelf2905 I’d be happy to turn this into a clean, repeatable experimental framework rather than a one-off analysis.

@jayeshs013

Hi, I’d be happy to work on this project. I have experience with **Python, machine learning, data preprocessing, statistical analysis, and reproducible experiments**. I can deliver: * A clean, reproducible polynomial-regression dataset * Preprocessing and feature-engineering pipeline * Systematic text-based prompt templates * MSE, MAE, R² and additional diagnostic metrics * Experiments across **2+ transformer-based models** * Reproducible Python/PyTorch code with seeds and README * Results tables, plots, and a concise technical report * Analysis focused specifically on **Data Preprocessing** and **Evaluation Metrics**, while keeping the framework extensible I can handle the project from **dataset preparation through experimentation, analysis, and final documentation**. Best regards, **Jey**

@ronakkataria1

Hi — in-context regression has one trap that decides whether the study is publishable: contamination. Polynomials from common seeds or textbook coefficients may already sit in pretraining data, and then you're measuring recall, not in-context learning. I'd generate synthetic polynomials with randomised coefficients per run, holding the generator seed separate from the experiment seed — reproducible without being memorisable. On your two axes: Preprocessing — for text prompts the real variable is numeric representation: decimal places, scientific notation, comma vs newline separation. Tokenisers split numbers inconsistently, so "3.14159" and "3.14" differ in token count, not just precision. I'd make numeric formatting a first-class preprocessing axis. Evaluation — MSE/MAE/R2 as specified, but R2 misleads on extrapolation, which is exactly where polynomial prompting fails. I'd report in-range and out-of-range separately, plus a degradation curve against degree. That diagnostic generalises to your other four axes. Deliverable: reproducible PyTorch code, fixed seeds, README, notebook with methodology, tables, plots, interpretation. Questions: 1. Which two models — open weights (Llama, Qwen) or API? 2. Polynomial degree range and samples per prompt? 3. Expected hours for this phase? Ronak — 8+ yrs; LLM evaluation, LLMOps, Python/PyTorch.

@asrawat100

Hi, I can build the dataset, preprocessing pipeline, prompt templates, and evaluation framework, then run reproducible experiments across two transformer models. I’ll deliver clean Python code, metrics, plots, and a concise technical report with actionable insights.

@anas389

Hi, I can build a reproducible experimental pipeline focused specifically on Data Preprocessing and Evaluation Metrics, while keeping the design extensible for the remaining axes. My approach would include: 1) Curating/generating a controlled polynomial-regression dataset with documented preprocessing and feature engineering. 2) Designing prompt templates that systematically vary preprocessing and evaluation-related instructions. 3) Implementing the full evaluation pipeline in **Python with PyTorch/JAX**, including MSE, MAE, R² and useful diagnostic statistics. 4) Running controlled experiments across **two transformer-based language models** with fixed seeds and consistent data splits. 5) Producing reproducible tables, plots and cross-model comparisons. 6) Delivering a Jupyter Notebook/Markdown technical report with methodology, implementation, results and actionable interpretation. 7) Providing a clear README with environment setup, seeds and exact commands to reproduce results. I’ll ensure train/validation/test separation prevents leakage and that the experimental design clearly isolates the two target axes. Deliverables: Code + README + experiments + results + technical report I’m ready to start by defining the dataset, experimental matrix and evaluation protocol before running the full benchmark.

@sangajay0

If you’re in college and looking for the best guidance, you’re in good hands! With the right trainer, support, and direction, you can build skills, confidence, and a successful career.

@rana0180

Hello, I’m a Computer Science and AI student with hands-on experience in Python, Machine Learning, regression analysis, data preprocessing, model evaluation, and data visualization. I can help structure the polynomial regression experiments, prepare and document the dataset, implement preprocessing and train/validation/test splits, and build a reproducible evaluation pipeline using MSE, MAE, and R². I can also organize the prompt templates and experiment results clearly, with tables, plots, and a concise technical report in Jupyter Notebook or Markdown. I’m comfortable working with Python and ML workflows, and I’m interested in applying these skills to prompt-based regression experiments and comparing the resulting behaviour across transformer-based models. I will keep the implementation reproducible by documenting seeds, preprocessing decisions, experimental settings, and evaluation procedures. I can provide clean, well-documented code and a clear README so the experiments can be reproduced on your machine.

@luckygedam17

i am an ai engineer i know large language models , ai models, lang chains very well , and i know ai