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

@ZohaibRoy

⭐⭐⭐⭐⭐ Create an AI-Based Predictive Maintenance Framework for Transformers ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for help with your Master's thesis in Electrical Engineering. Zohaib is here to assist you! My team has successfully completed 50+ similar projects in predictive maintenance. I will implement machine learning models, optimize maintenance scheduling, and ensure quality data handling for your project. ➡️ Why Me? I have 5 years of experience in machine learning and predictive maintenance. My skills include Python programming, model evaluation, and data handling. I also have a strong grip on optimization libraries and data analysis, which will be crucial for your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I'm excited to help you succeed with your thesis! ➡️ Skills & Experience: ✅ Python Programming ✅ Machine Learning ✅ Data Handling ✅ Feature Engineering ✅ Hyperparameter Tuning ✅ Model Evaluation ✅ Synthetic Data Generation ✅ Optimization Techniques ✅ Monte Carlo Simulation ✅ Jupyter Notebook ✅ Data Analysis ✅ Documentation Waiting for your response! Best Regards, Zohaib

@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 you need help with your Master's thesis in Electrical Engineering focused on predictive maintenance for distribution transformers. I can assist with completing and fine-tuning your Random Forest, implementing the SVM with RBF kernel, and benchmarking with Decision Trees. I will also manage your BRAVO dataset effectively, handle missing data, and engineering the key features like Load Stress and Failure Risk Score. For synthetic data, I will generate fault scenarios using Monte Carlo simulation and validate them properly. On the optimization side, I’ll develop scheduling models using integer programming and integrate machine learning results to improve maintenance decisions. Lastly, I will help analyze the outcomes with clear visuals and support your cost-benefit and sensitivity analysis. I will deliver fully documented Python code that matches your thesis needs. I look forward to working together. Could you share the current state of your Random Forest implementation and any specific challenges you are facing with it? Thanks,

@koolvishruth

Hello, I am Vishruth from Banglore,India.I am Machine Learning Engineer. Thank you for considering my proposal. I have carefully reviewed your project requirements and believe my 8+ years of real-world and freelance experience make me a suitable candidate for this project. I would like to connect with you in chat to discuss your project in more detail. Regards,
Vishruth

@pkundu25

I am an experienced Data Science and Machine Learning Expert. Your job caught my eye and looks to be quite interesting to me as I developed unsupervised predictive maintenance models in recent past. I am well conversant with Generative AI and hands-on experience in developing AI applications using LangChain and LLMs. I am confident that I will be able to help you by developing AI-powered predictive maintence framework for distribution transformers with optimized maintenance scheduling. Similar work done in the past: - Unsupervised predictive maintenance models - Multiclass intent classification - Ground water quality prediction - AI Powered Copilot for Text2SQL Query - Semantic search engine - Topic modeling Relevant Skills: - Python - Numpy, Pandas, Scikit learn - Supervised and unsupervised ML algos - GPT4o/Gemini/Llama3.2 - MySQL - TensorFlow - Google Colab - OpenCV Let's have a chat to understand the project objective and the dataset in details. I assure you to deliver the best quality results and ensure the customer satisfaction. Looking forward to hearing from you soon. Thanks for the opportunity.

@shadabkhan92

Done lot of similar project lets discuss As an AI systems builder in the domain of Electrical Engineering with significant Python expertise, I am the ideal candidate to handle your Master’s thesis project on predictive maintenance for distribution transformers. My experience crafting production-ready AI systems makes me sensitive to the unique challenges underlying real-world implementation. I possess significant proficiency in machine learning models, as well as a deep understanding of scikit-learn, pandas, and numpy, which are crucial for handling your BRAVO dataset with over 15,000 samples and 16 features. Furthermore, I am no stranger to optimization libraries such as PuLP or OR-Tools - essential for developing your maintenance scheduling models. Delving further into my skill set, I leverage Jupyter Notebook proficiently and hold an appreciation for rigorous model evaluation through extensive hyperparameter tuning, cross-validation and performance metrics assessment. Overseeing synthetic data generation based on DGA gas ratios will be handled meticulously via Monte Carlo simulation, validation through tests like KS test would undoubtedly maximize results integrity. Let's work together to create cutting-edge engineering that makes a positive impact for generations to come!

@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

I can help with this, I will implement and refine your Random Forest and SVM (RBF kernel) models, build the Monte Carlo-based synthetic fault data pipeline using IEC DGA gas ratios, and develop the integer programming maintenance scheduler integrated with ML predictions. One thing worth considering — for the KS test validation of synthetic data, I will also run a feature-wise correlation check against the original BRAVO dataset. Synthetic samples that pass marginal distribution tests can still break inter-feature relationships like Load Stress vs. DGA ratios, which would skew your Failure Risk Score and downstream scheduling results. Questions: 1) Is the Random Forest already producing baseline metrics, or does it still need debugging before hyperparameter tuning? Looking forward to your response. Best regards, Kamran

@iglobalvn

I can help you turn this thesis into a solid, defensible predictive maintenance workflow — from model tuning to maintenance scheduling and thesis-ready results. Your project is a strong fit for my background in Python-based ML, engineering datasets, and optimization. I’ve worked on predictive modeling pipelines where accuracy is not enough — the real value comes from feature engineering, validation, and translating model outputs into operational decisions. What I’ll bring: - Random Forest + SVM implementation with proper tuning, 5-fold CV, and evaluation (Accuracy, F1, AUC) - Engineering-focused feature work on transformer health indicators like load stress, overdue maintenance, and risk scoring - Optimization/simulation support using PuLP or OR-Tools to compare baseline, greedy, and IP-based scheduling I’m comfortable working in Jupyter with scikit-learn, pandas, numpy, and optimization libraries, and I can structure the code and outputs so they are cleanly usable in your thesis. If needed, I can also help validate synthetic fault scenarios and present the methodology clearly for academic writing. My approach: review your current Random Forest work, finalize the ML pipeline, validate data handling, build the synthetic scenario and scheduling layer, then package everything with graphs, metrics, and concise thesis explanations. If you’d like, I can start by reviewing your current notebook and outlining the fastest path to a complete, high-quality result.

@glanegonsalves

Hey there Glane here,hope you're doing well. I can help you in end to end ml analysis on your Bravo dataset focusing on accuracy,precision,recall,f1 score,f2 score etc while hyperparameter tuning the desired models, taking care of the imbalanced data and also optimising the desired variables. All these will be done using Python via JupyterNotebook. Feel free to get in touch.

@FaizalShaik80

Hello, hope you are well. I have reviewed your project and noticed that it is very similar to a task I completed two months ago. I am an experienced and specialized freelancer with 6+ years of practical experience in Python and I’m able to complete and deliver this project promptly. You can visit my profile to check my latest work and recent reviews. Let us make this great together, please connect in chat. Talk soon.

@vishal1145

Your predictive maintenance framework will fail validation if the synthetic DGA data doesn't match real-world gas ratio distributions - I've seen thesis committees reject models that can't prove statistical similarity between Monte Carlo outputs and actual transformer failure patterns. Quick question - what's your current class imbalance ratio after SMOTE, and are you validating on pre-SMOTE data? Most students oversample before the train-test split, which inflates accuracy by 15-20% and gets flagged during defense. Here's the implementation approach: - RANDOM FOREST + SVM: Implement ensemble stacking where RF handles non-linear feature interactions and SVM-RBF captures decision boundaries. I'll add SHAP values to explain which DGA ratios drive failure predictions - thesis committees love interpretability. - MONTE CARLO SIMULATION: Generate 5000+ synthetic fault scenarios using IEC 60599 gas ratio constraints, then validate with Kolmogorov-Smirnov tests to prove the synthetic distribution matches your BRAVO dataset's statistical properties. - INTEGER PROGRAMMING: Build a PuLP-based scheduler that minimizes total maintenance cost while respecting transformer criticality scores and predicted failure probabilities. I'll benchmark against greedy heuristics to show 10-15% cost reduction. - FEATURE ENGINEERING: Calculate Load Stress using thermal aging models and Failure Risk Score using weighted DGA thresholds. I'll document the engineering rationale so you can defend every variable during your viva. I've built 4 predictive maintenance systems for industrial clients using scikit-learn and optimization frameworks. I don't take on thesis projects where the methodology isn't defensible - let's schedule a 20-minute call to walk through your dataset and confirm the Monte Carlo parameters align with IEC standards.

@anchalsingh0005

Drawing from my 7+ years of software development experience, specifically in the field of machine learning and artificial intelligence, I am confident about fulfilling your specific needs for your Master's thesis on a predictive maintenance framework. I have hands-on expertise with Python libraries like scikit-learn, pandas, and numpy which underpin the core functionalities of the ML models you are working with (Random Forest, SVM, Decision Tree) as well as optimization libraries you’ve mentioned like PuLP or OR-Tools. Moreover, having experience in engineering datasets in general and time series data processes specifically provides me insights into power systems which can further strengthen the contribution I bring to your thesis. I will approach this project not just as a task but as an opportunity to create meaningful work that aligns effectively with your specific academic goals. Let’s have a conversation about how we can leverage your dataset to develop meaningful insights that go beyond just meeting targets.

@mobiwebsolutions

✋ Hi There!!! ✋ The Goal of the project:- Develop an AI based predictive maintenance system using Random Forest and SVM with optimization driven scheduling for transformers. I carefully read your full thesis requirements including ML models, BRAVO dataset handling, feature engineering, Monte Carlo simulation, and optimization integration, and I understand the need for accurate and well documented implementation. I am the best fit as I combine machine learning, data engineering, and optimization experience for research level projects. * Implementation and tuning of Random Forest, SVM, and evaluation with cross validation * Advanced data processing, feature engineering, and synthetic data generation * Optimization models with scheduling integration and performance analysis I provide UI design, database management, testing, ML pipeline setup, and full source code delivery with documentation. I have 9+ years experience and have completed similar predictive maintenance and time series ML projects. Looking forward to chat with you for make a deal Best Regards Elisha Mariam!

@Zawiya3

As an engineer with a strong background in both electrical and software engineering, I am confident that I have the necessary expertise to assist you with your Master's thesis focused on predictive maintenance for distribution transformers. My proficiency in Python and libraries like scikit-learn, pandas, and numpy, which are essential for deploying machine learning models aligns directly with the technical requirements of your thesis. Having delved deep into data science and machine learning domains for many years now, I possess a considerable amount of experience handling large datasets and implementing models like Random Forests, Support Vector Machines (RBF kernel), and Decision Trees - all of which are integral to your project implementation. Additionally, I'm also well-versed in handling missing data and addressing class imbalance through techniques like SMOTE, skills that will be necessary given the nature and size of your dataset.

@ashinnd84

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

@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.

@KolmanOuassou

Hi, I am a data analyst/statistician and Economist with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.

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