Need Expert in Machine Learning (Random Forest + SVM) for Electrical Engineering Thesis (Predictive Maintenance + Simulation)

@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

@AITSoft

Hello, I understand your project is about improving predictive maintenance for transformers with machine learning and optimization. You need help finishing Random Forest and SVM models, tuning parameters, and making sure they perform well using tests like cross-validation. I will also work with your dataset to handle missing data and class imbalance, and ensure features are strong. On top of this, I can use Monte Carlo simulation to create more fault data following IEC rules, and check its accuracy. For scheduling maintenance, I’ll build models that use ML results for better decisions and compare them with traditional methods, aiming to improve cost efficiency by at least 10-15%. I will deliver clean code, results with charts, and clear explanations useful for your thesis. What format is the BRAVO dataset in, and is it preprocessed or raw data? Do you have specific performance targets or benchmarks you want for the ML models? Are you expecting the Monte Carlo simulation to generate data for certain fault types only? What are your priorities between model accuracy and interpretability in maintenance scheduling? Do you have existing Python code for the optimization models or should I develop them from scratch? Thanks,

@ShaikhAneesa

Hi there, I’ve reviewed your requirements and understand the need for a high-integrity technical layer to transform your BRAVO dataset and transformer metrics into a fully synchronized predictive maintenance engine. I am confident I can architect a robust system utilizing Python, scikit-learn for ensemble modeling, and PuLP for integer programming to ensure your failure risk scores and maintenance schedules run with absolute technical precision. My approach begins with a structural audit of your current feature engineering to ensure a "Source of Truth" for load stress and maintenance history. I will engineer the data processing layer to handle Monte Carlo fault simulations and SMOTE class balancing, using Python for the multi-step orchestration of cross-validated ML models and optimization algorithms. Next, I will implement a "resilient-by-design" evaluation framework, documenting cost-benefit metrics and sensitivity analysis directly within your results to prevent data siloing. Finally, I will deliver backend-ready Jupyter Notebooks with commented logic and clear methodology breakdowns, ensuring you are fully equipped for your thesis defense. Would you like the maintenance scheduling to focus strictly on cost minimization, or should we include a weighted safety constraint? Let’s get started on pushing your predictive maintenance simulation live. Warm regards, Aneesa.

@nrajuu07

I’m Naga Raju, holding a Master’s degree in Data Science and a Bachelor’s degree in Electrical Engineering, with strong expertise in machine learning, data analytics, and engineering systems. I specialize in Python-based predictive modeling, optimization, and technical research projects, combining domain knowledge from power systems with advanced AI techniques. I can support thesis and industry projects through clean implementation, model development, data analysis, simulations, and well-structured technical documentation.

@kamran2012

Interesting project, I will finalize your Random Forest and SVM (RBF kernel) models, run GridSearchCV with 5-fold cross-validation, and deliver the optimization-based maintenance scheduling using PuLP — all producing the evaluation metrics and comparison graphs you need for your thesis. One thing worth considering: before generating synthetic fault data via Monte Carlo, I will validate that your DGA gas ratio distributions pass the KS test against real IEC reference ranges. If the synthetic samples drift outside realistic bounds, your SVM decision boundary will shift and hurt generalization on actual transformer data. Questions: 1) Is the BRAVO dataset already cleaned and SMOTE-applied, or do you need me to handle that pipeline from the current raw state? Ready to start whenever you are. Kamran

@mobimubasir

Hello there, I’m a reliable and detail-oriented professional with experience delivering high-quality results across a range of projects, ensuring accuracy, efficiency, and clear communication throughout. I focus on understanding your exact requirements and providing solutions that are practical, well-structured, and ready for immediate use or implementation. I can start right away and deliver within your timeline while maintaining strong attention to quality and consistency.

@iglobalvn

I can turn your BRAVO-based predictive maintenance thesis into a clean, defensible ML + optimization workflow that is ready for analysis and writing. Your project needs someone who can bridge power-system context with practical Python implementation — from model tuning to maintenance scheduling. I’m a strong fit because I work comfortably across scikit-learn, pandas/numpy, and optimization tools like PuLP/OR-Tools, and I understand how to structure results for an engineering thesis. For this project, I’ll focus on: 1) finalizing Random Forest and RBF-SVM with proper tuning and 5-fold CV, 2) refining feature engineering and imbalance handling without distorting the dataset, and 3) integrating ML outputs into baseline, greedy, and integer-programming maintenance schedules. Relevant strengths: predictive maintenance modeling, synthetic data validation, and performance reporting that supports thesis-level conclusions. I can also help with Monte Carlo fault generation, KS-based distribution checks, and clear comparison plots for AI vs traditional maintenance. My approach: review your current notebook/code, stabilize the data pipeline, benchmark models, build the simulation/optimization layer, then package everything into well-documented Python code plus a concise methodology summary for your thesis. If you’d like, I can review your current files and outline the fastest path to completion.

@vishal1145

Your scheduling optimization will fail if the ML model's false positive rate exceeds 15% - you'll dispatch maintenance crews to healthy transformers and blow your operational budget. I've built predictive maintenance systems for industrial clients where misclassification costs $5K per truck roll. Before finalizing the architecture, I need clarity on two things: What's your current Random Forest precision/recall breakdown per class? And does your university's compute environment support parallel GridSearchCV, or are we constrained to local execution? Here's the technical approach: - RANDOM FOREST + SVM: Implement ensemble voting with calibrated probabilities using CalibratedClassifierCV to reduce false positives below 12%, then benchmark against your existing RF baseline using stratified K-fold to prevent data leakage. - SMOTE + CLASS IMBALANCE: Your 60:40 ratio might be over-sampled - I'll run ablation studies comparing SMOTE, ADASYN, and class weights to find the sweet spot where F1 score peaks without overfitting to synthetic samples. - MONTE CARLO DGA SIMULATION: Generate 5K synthetic fault samples using IEC 60599 gas ratio distributions, validate with Kolmogorov-Smirnov tests, and inject controlled noise to match real-world sensor variance. - INTEGER PROGRAMMING OPTIMIZATION: Build a PuLP-based scheduler that minimizes cost while respecting crew availability constraints and transformer criticality scores from the ML pipeline - I'll compare greedy vs optimal solutions to quantify the efficiency gap. - POWER SYSTEMS DOMAIN: I've worked on grid reliability projects where DGA analysis directly fed into asset management decisions - I understand why H2/CH4 ratios matter for thermal faults. I've delivered 4 ML-based predictive maintenance systems where model outputs drove real operational decisions. Let's schedule a 20-minute call to review your existing RF code and align on evaluation metrics before I start tuning hyperparameters.

@KirtikaKataria

Hi there, I understand you’re working on an Electrical Engineering thesis focused on AI-based predictive maintenance for transformers, and you need support finalizing the ML models, integrating them with optimization-based scheduling, and producing solid, thesis-ready results. My approach will begin with refining your existing models completing Random Forest, implementing SVM (RBF), and optionally Decision Trees followed by structured hyperparameter tuning and 5-fold cross-validation to ensure robust performance (Accuracy, F1, AUC). I’ll then validate and enhance your data pipeline, including handling missing values, improving feature engineering, and ensuring SMOTE outputs remain statistically sound. For synthetic data, I’ll implement Monte Carlo simulation based on DGA ratios and validate distributions using statistical tests. Finally, I’ll build and compare scheduling models (baseline, greedy, and integer programming via PuLP), integrating ML outputs to demonstrate measurable improvements, supported by cost-benefit and sensitivity analysis. Deliverable: Clean, well-documented Python notebooks with models, simulations, evaluation metrics, and clear explanations aligned with thesis requirements. QUESTION: For the final evaluation, should the optimization prioritize minimizing failure risk, minimizing maintenance cost, or a weighted balance of both? Let’s get your thesis to a strong, submission-ready state. Regards, Shehwani.

@mobiwebsolutions

✋ Hi There!!! ✋ The Goal of the project:- Develop a predictive maintenance system for distribution transformers using Random Forest and SVM with optimization based scheduling. I have carefully reviewed the BRAVO dataset processing, ML modeling, simulation, and optimization requirements. I am the best fit for this project due to 9+ years experience in machine learning and engineering analytics. * Random Forest, SVM (RBF) and Decision Tree with GridSearchCV, cross validation and performance metrics * Data preprocessing including missing values handling, SMOTE balancing and feature engineering refinement * Monte Carlo synthetic data generation and optimization based scheduling using PuLP or OR-Tools I will provide UI design, database management, testing, well documented Python code, and full source code delivery at completion. I have completed similar predictive maintenance and power systems optimization projects. Looking forward to chat with you for make a deal Best Regards Elisha Mariam!

@ashinnd84

Hello there, we are a team of developers and we can do this project in no time. Please, send me a message to discuss the work. Thanks Ashish Kumar.

@geologistt

With over a decade of solid academic writing, tutoring, and Python development, A2Z Research Consultants is excellently positioned to handle your machine learning project. Our team's experience in data analysis and electrical engineering will be a great asset in your thesis work involving power systems and predictive maintenance. We've successfully edited thousands of academic papers, ensuring clarity, effectiveness and adherence to a scholarly standard - attractive qualities for this essential section of your work. As you seek an expert in machine learning models - random forest, SVM, as well as decision tree and maintaining data quality - our extensive Python skills combined with scikit-learn's libraries make us not only qualified but ever ready to tackle the job. Our capability to perform hyperparameter tuning, 5-fold cross validation, and performance evaluation using Accuracy, F1, AUC will make sure that the outputs generated are reliable and useful for your comparison goals. Moreover, dealing with BRAVO dataset of over 15k samples and the synthetic data generation through Monte Carlo simulation are additional areas of strength for us. Also an area we specialize in is optimization with tools like PuLP or OR-Tools which are perfect for maintenance scheduling crucial to the success of this project.

@ionstefanache1

Hi AU, I can do that edu-project for you. I have a great experience in this kind of projects Please contact me for more details Thanks

@divyam12345

Dear Sir/Madam, I have a strong background in Electrical Engineering and experience with machine learning and optimization. I understand your project requirements and can help you implement models, handle data, and complete the analysis clearly. I am confident I can support you throughout your thesis work. I will provide clean, well-documented code and clear results with proper explanations. I can also guide you step by step to meet your deadline. Before we start, let’s have a quick chat or call to understand everything clearly. Let’s connect in the chatbox to discuss the project further, including the budget and timeline. I am ready to work with you, please connect in the chatbox for further discussions. Thank You. Dr. Divya.

@chiragardeshna

Hi there, Strong alignment with this project comes from experience delivering machine learning solutions for predictive maintenance and engineering-focused data modeling with reproducible research workflows. Clear understanding of the requirement to implement Random Forest, SVM (RBF), and benchmarking models, along with feature engineering, synthetic data generation, and optimization-based maintenance scheduling. Hands-on expertise with Python, scikit-learn, pandas, NumPy, and optimization tools ensures accurate modeling, cross-validation, and well-structured simulation pipelines. Risk is minimized by validating data distributions, tuning models carefully, and ensuring all outputs are consistent, documented, and thesis-ready. Available to start immediately happy to share a quick demo or discuss next steps. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@mehwishb3

As an engineering expert I want to offer my services for your machine learning thesis. Having prior experience in such projects, I assure its perfect completion. Let's connect.

@abdf2010

Hello, I am interested in your project, Need Expert in Machine Learning (Random project. I've successfully completed projects involving Python, Engineering, Matlab and Mathematica before. Happy to discuss the details whenever works for you.

@mughiraaa

Hello, I have reviewed the details of your project. i will review your bravo dataset in python using pandas and numpy to clean missing values and confirm class balance after smote, then refine feature engineering fields such as load stress and failure risk score before training models using scikit learn where i will complete random forest and svm with rbf kernel and also include decision tree for comparison, i will run gridsearchcv and randomizedsearchcv with 5 fold cross validation to tune parameters and evaluate results using accuracy f1 and auc so you get clear model performance, after that i will generate synthetic fault data through monte carlo simulation based on iec dga gas ratios and validate distributions using statistical tests such as ks test, then i will connect model outputs to a maintenance scheduling layer where i will code a baseline logic followed by greedy and integer programming using pulp to optimize scheduling decisions. Let's have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@huonghy89

Hi, I hope you are doing well. Very happy to bid your project because my skills are fitted in your project. I have strong experience in Python-based machine learning for time-series and tabular data, including Random Forest, SVM, feature engineering, and model evaluation, along with optimization techniques using PuLP/OR-Tools. I have also worked on predictive maintenance and simulation-based analysis, delivering clean, well-documented code and research-oriented outputs suitable for academic projects. I will finalize and optimize your ML models (Random Forest, SVM, Decision Tree) with proper hyperparameter tuning, cross-validation, and evaluation metrics while refining feature engineering and handling data quality issues. I will generate synthetic fault data using Monte Carlo methods based on DGA standards, validate distributions, and integrate results into a maintenance scheduling framework using baseline, greedy, and integer programming approaches. I will also provide complete analysis including cost-benefit and sensitivity studies, along with clear documentation and visualizations ready for your thesis. If you send the message, we can discuss the project more. Thanks.