Revise and optimize an existing MSc machine learning thesis project

@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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@brashel

Hello, I can help audit and improve your MSc machine-learning thesis, including preprocessing, leakage checks, SMOTENC, model tuning, validation, code corrections, results interpretation, and aligning the thesis write-up with the final methodology. I’ll keep the workflow reproducible and academically defensible.

@maxscender

I can help you. I’ll first audit the existing pipeline for data leakage—checking any inadvertent use of target info in preprocessing or resampling—then correct SMOTENC application so it fits only on training folds. After that, I’ll re-run tuning with proper nested or cross-validated search, fix validation metrics, and ensure the code produces reproducible results. Finally, I’ll update the thesis text to match the corrected methodology and outcomes, keeping the narrative consistent with the actual implementation. I work methodically, documenting every change so you can see exactly what was fixed and why.

@mattafareed4848

Hello I can optimize your MSc machine learning thesis, including preprocessing, leakage removal, SMOTENC, model tuning, validation, code corrections, results, and corresponding thesis updates. Regards Muhammad

@rezamahi

I can revise and optimize your MSc machine learning thesis project end-to-end, while preserving the existing research objective and methodology where appropriate. I’ll focus on leakage detection/removal, robust preprocessing, correct SMOTENC implementation, train/validation/test separation, model tuning, cross-validation, evaluation, and fixing any issues in the existing code. I’ll also update the corresponding thesis methodology, results, tables/figures, and discussion so the written work accurately reflects the revised experiments. I’ll keep the workflow reproducible and clearly document what was changed and why. I can start by reviewing the current code, dataset pipeline, and thesis to identify the highest-impact issues first.

@toriquldev123

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@LeadForce1

Hi, "Leakage-Safe Preprocessing + Model Validation" - your MSc project needs technically sound data processing and reliable evaluation. I can review the existing Python workflow, identify data leakage, correct preprocessing order, configure SMOTENC only within the training process, and improve model tuning and validation. I can also check the results and code for consistency so the reported metrics match the actual experiments. I can explain the technical changes and help you update your own thesis based on the corrected results, rather than inventing results or research findings. The key edge case is preventing oversampling or preprocessing from leaking information from validation data into training. Can you share the current notebook/code and dataset structure so I can identify where the leakage occurs? Looking forward to working with you. Luigi Tedy

@codeandcommand

Preprocessing and leakage are the two things that actually make or break a thesis result, and it sounds like that's exactly where this one needs work. I would go through the pipeline stage by stage, fix any leakage between train and test splits, and tighten up the feature handling. Can start today, roughly 3 days for a full pass. These are starting numbers based on the description, we can lock in specifics once I see the notebook. Want me to take a quick look first?

@vzaretsky

The mention of leakage removal alongside SMOTENC is the part that will actually determine how much rework this needs — if the class balancing was applied before the train/test split, every downstream metric in the thesis is inflated and the results section has to be redone, not just the code. My approach: audit the preprocessing pipeline first to confirm where SMOTENC sits relative to the split, restructure it into a proper pipeline (fit resampling only on training folds, likely with imblearn's Pipeline to prevent leakage during cross-validation), rerun model tuning and validation with the corrected flow, and regenerate the results tables and figures so the thesis text matches the corrected numbers. One question: is the leakage you've identified specifically the SMOTENC-before-split issue, or is there also feature-level leakage (e.g. target-derived or time-based features) I should check for separately, since that changes how deep the preprocessing rewrite needs to go? Send me the current notebook/scripts and the leakage details you've already found and I'll start immediately.

@bilalmalik746429

Hi there, I’m Bilal, a Software Engineer with strong experience in Python, data processing, machine learning workflows, and building reliable data-driven solutions. I believe every project is not just about writing code but creating real value through accurate and reproducible results. With experience in Python, Pandas, NumPy, Scikit-learn, data preprocessing, feature engineering, SMOTE/SMOTENC, model tuning, cross-validation, and evaluation, I can help revise and strengthen your existing MSc machine learning project. My focus is always on: Clean, reproducible, and well-structured code Correct preprocessing and validation methodology Clear documentation and alignment between code and results For your project, I can review the existing pipeline, identify and remove data leakage, correctly implement SMOTENC within the training process, optimize model parameters, improve validation methodology, and verify that reported metrics and results accurately reflect the updated implementation. I can also update the corresponding thesis sections so the methodology, experiments, tables, and conclusions remain consistent with the revised work. ✨ "I aim to turn complex technical work into accurate, reproducible, and clearly presented results." ✨ Best regards, Muhammad Bilal

@engruhulajom

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@adriank06

Hi, I will do and revise your project based on your task type using scikit-learn and smote library and other features selection and extraction inside your project

@AndrewJSMAN

I've architected MSc-level ML pipelines with preprocessing, leakage checks, and SMOTENC before. This is right in my wheelhouse. I'll audit the existing pipeline for leakage, update preprocessing to handle mixed categorical/numerical features, re-implement SMOTENC with fixed random_state, tune models via Optuna with pruning, validate via nested cross-validation, and regenerate results tables. Code will be PEP8-compliant with type hints and logging. Thesis updates will align with revised methodology. I can start immediately. Thanks, Andrii.

@naeem1114

If the current preprocessing pipeline still lets target leakage sneak into training folds, the validation scores will be overly optimistic. I'll rewrite the pipeline to enforce strict separation, using sklearn's ColumnTransformer and a custom leakage guard before SMOTENC is applied. That way the synthetic minority samples are generated only after the split, keeping the test set untouched. A common pitfall is tuning hyper-parameters on data that already includes SMOTENC-generated rows, which can bias the search. I can start right away and have the revised code and updated thesis sections ready for review.

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@rofeald

With strong experience in Python, machine learning, data preprocessing, augmentation, model tuning, and validation, I can refine your existing MSc thesis project while keeping the methodology technically sound and reproducible. I’ll first audit the current preprocessing and modeling pipeline to identify data leakage, incorrect transformations, inconsistent train/test handling, and implementation issues. From there, I’ll restructure the workflow so preprocessing and SMOTENC are applied correctly without allowing information from validation or test data to influence training. For model optimization, I’ll review the existing algorithms, hyperparameters, evaluation metrics, and validation strategy, then implement appropriate tuning and compare results consistently rather than optimizing against a single split. The key challenge is ensuring improved performance is genuinely valid rather than caused by leakage or overly aggressive augmentation. I’ll maintain a reproducible pipeline with clear experiment tracking and defensible evaluation. I’ll also correct the associated code and update the relevant thesis sections so the methodology, experiments, results, and conclusions accurately reflect the revised implementation. Regards, Rofeal

@durgesh8527

As an experienced AI developer specializing in Machine Learning solutions and a member of a proficient Full-Stack Development team with numerous successful projects under our belt, we are well-equipped to handle your MSc machine learning thesis project. Drawing from my mastery in Python, a language synonymous with machine learning, I guarantee efficient preprocessing techniques that will enhance the accuracy of your data analysis. In addition to preprocesssing, I excel in leakage removal and SMOTENC: two key components required for consequent model validation; these are areas that I can further strengthen your thesis study. Furthermore, my expertise in model tuning ensures optimal performance, achieving the full potential of your research. I understand that your research is central to your academic journey, and thus as your chosen freelancer, I commit to conducting extensive code corrections and meticulous validation ensuring accurate results that bolster the integrity of your thesis findings. Partnering with me means a timely delivery without compromising quality; let's optimize your existing MSc machine learning thesis together!

@krupnovdenis

Hello! We can revise and optimize your ML thesis project end to end. 1. What is the main priority for the revision: code, results, or thesis text? 2. Do you already have the current codebase and thesis draft? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@ujjwalmahar01

I will start by auditing your current pipeline to identify where data leakage is occurring between your training and validation sets. This is the most common reason for inflated results in MSc projects and needs to be addressed before we touch the model architecture. Once the data integrity is verified, I will refactor your preprocessing workflow to integrate SMOTENC correctly within a cross-validation pipeline. This ensures your synthetic samples do not influence the validation metrics. For the model tuning, I will implement a structured hyperparameter search to replace any manual configurations, ensuring the final results are reproducible and statistically sound. Regarding your thesis updates, I will align the technical methodology sections with the optimized code to ensure your narrative matches the empirical findings. My experience with Python data pipelines and automated processing helps me maintain a clear link between the code output and the final document. Are you available for a brief call to discuss the current state of your codebase and the specific metrics you are trying to improve?