Structured Data ML Model Development

@Vasilchenko

Hi, I can build a clean Python ML pipeline for your structured CSV/database data, covering feature engineering, model training, hyperparameter tuning, evaluation, and serialized model outputs for future re-training. I will compare suitable classification/regression models such as scikit-learn and XGBoost, provide a concise performance report with the right metrics, and optionally prepare a Power BI dashboard to show key findings and predictions for stakeholders. https://www.freelancer.com/u/Vasilchenko

@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

@nrajuu07

I'm a data scientist experienced in end-to-end ML pipelines and Power BI dashboarding. I'll handle the full workflow — feature engineering, model training, hyperparameter tuning, and evaluation using scikit-learn, XGBoost, or equivalent — delivering well-documented notebooks, a clear model comparison report, serialized model files with retraining instructions, and an optional Power BI dashboard ready for stakeholder review. Clean, analyst-friendly code that outperforms your current baselines. Ready to start immediately.

@anchalsingh0005

As an accomplished freelancer with over 7 years of experience in full stack development and data analytics, I believe I'm the perfect candidate to tackle your Structured Data ML Model Development. I've not only developed using Python, but also have hands-on experience with all aspects of machine learning pipeline such as Pandas, scikit-learn, xgboost and more. These skills are crucial for robust classification and regression models that extract meaningful insights from structured data. In terms of evaluation, you can rely on my expertise to provide a comprehensive performance report, comparing models using key metrics which align with your targets like accuracy, AUC, or RMSE. Additionally, I understand the importance of documentation and reproducibility; therefore, I will provide well-documented Python notebooks covering the entire modelling pipeline.

@khaliduom103

‎Hi i am an experienced python/Matlab developer with PhD in applied mathematics and data analyst.I can help you Matlab/python coding, optimization, numerical analysis،simulation and stock data prediction of the stock data. ‎p ‎

@vishal1145

Your current baseline models will degrade the moment your data distribution shifts—and without a monitoring layer, you won't know until stakeholders start questioning the predictions. Most teams discover this 3 months into production when accuracy drops from 92% to 74% and nobody can explain why. Before I architect the pipeline, I need clarity on two things: What's your current retraining cadence, and do you have labeled data from the last 6 months to validate model stability? Second, are these predictions feeding automated decisions or human review workflows? That determines whether we optimize for precision or recall. Here's the technical approach: - PANDAS + SCIKIT-LEARN: Build a feature engineering pipeline with automated outlier detection and missing value imputation strategies that your analysts can modify without breaking downstream models. - XGBOOST + HYPEROPT: Implement Bayesian optimization for hyperparameter tuning instead of grid search—cuts tuning time from 8 hours to 45 minutes while finding better configurations. - MODEL VERSIONING: Set up MLflow tracking so every experiment logs metrics, parameters and artifacts automatically. Your team can compare 20 model versions in 2 minutes instead of digging through notebooks. - POWER BI INTEGRATION: Deploy a REST API wrapper around the serialized model so Power BI can pull live predictions without manual CSV exports. Include confidence intervals so stakeholders know when predictions are uncertain. - DRIFT MONITORING: Add statistical tests (KS test, PSI) that flag when incoming data diverges from training distributions—prevents silent model failures. I've built 8 production ML systems for clients in retail and fintech where model performance directly impacts revenue. The difference between a notebook that works once and a system that runs reliably for 18 months is monitoring, versioning and clear handoff documentation. Let's schedule a 20-minute call to walk through your baseline metrics and data schema before I start development.

@Web1devloper

Drawing from my 8+ years of extensive experience in data analysis and science, which specifically includes data storytelling, dashboard development, machine learning and end-to-end data solutions, I'm confident that I possess the skills you require for this project. Having catered to various industries like finance, healthcare, e-commerce, and SaaS, I'm well-aware of the challenges involved in transforming complex datasets into valuable business insights. In terms of technical proficiency, I am highly skilled in using Python (including Pandas and scikit-learn) and I have a great understanding of machine learning algorithms such as XGBoost -- all crucial aspects required for developing your structured data ML model. I can provide you well-documented Python notebooks with the entire modeling pipeline covering feature engineering to final evaluation. Moreover, my strong command over Power BI combined with advanced Excel skills can ensure that not only will I be able to deliver on the performance reporting and serialized model files you need but also provide an optional Power BI dashboard surfacing key findings and live predictions – further enhancing the value you derive from your data. I believe joining forces could lead us to truly unlocking the full potential of your valuable data!

@KrisuTechkul

Hi, we are a team of 20+ AI/ML Engineers based in Delhi - have completed 300+ projects with 100% client satisfaction & long term association. With a robust background in Data Science and Machine Learning, I'm your ideal candidate for this project. My team and I have undertaken numerous projects involving all aspects of the modelling pipeline that you require, with a focus on using Python for data processing and libraries like pandas, scikit-learn, and XGBoost for machine learning tasks. We're adept at producing well-documented code for reproducibility and clarity while training models. Additionally, our capabilities extend beyond just model building. We are well-versed in analyzing and selecting the best performing models based on a range of metrics including accuracy, AUC, and RMSE. Producing concise yet comprehensive performance reports is our specialty. We also understand the importance of handover and always ensure that the serialized model files we provide are accompanied by clear re-training instructions.

@haitham1996

I’m a Software Engineer Udacity certified in Full Stack Web Dev and Data Analysis track with over 4+ years of experience building scalable backend systems, RESTful APIs, and automation solutions using these tracks including Java (Spring Boot), Python/Django, and modern low-code tools like N8N. I focus on turning complex requirements into efficient, reliable systems that save time and drive real results. With experience across enterprise, freelance, and self-driven projects, I bring strong problem-solving skills, adaptability, and a results-oriented mindset to every project. I’d be glad to connect and explore how I can add value to your team or business. Check my profile : https://www.freelancer.com/u/haitham1996?frm=haitham1996&sb=t

@sudhir0112

Hi, With 10 years of experience as a data analyst, I have strong expertise in the exact combination you need: Python-based analytics, machine learning model development, and Power BI visualization. I regularly work with structured tabular data from CSV exports and relational databases. My experience includes: - Feature engineering, data cleaning, and exploratory data analysis - Building classification and regression models using pandas, scikit-learn, and XGBoost - Creating well-documented Python notebooks with clear pipeline documentation - Developing Power BI dashboards to visualize model outputs and business metrics I understand that the heavy lifting here is on the ML side, and I can deliver robust, well-tested models that translate cleanly into business insights. I'll also ensure the Power BI layer makes the results accessible and actionable for non-technical stakeholders. I'm available to start immediately and can deliver within the project timeline. Let's discuss your specific datasets and business objectives!

@Visharaa2024

Hello, I’d be excited to support your data-driven initiatives by building robust machine learning solutions backed by clean, maintainable Python workflows and insightful Power BI visualisations. I have hands-on experience working with structured datasets from CSV exports and relational databases, developing end-to-end classification and regression pipelines using pandas, scikit-learn, XGBoost, and related tools. I can deliver well-documented notebooks/scripts covering feature engineering, preprocessing, model training, hyper-parameter optimisation, evaluation, and deployment-ready serialization. Along with the models, I’ll provide a concise performance comparison report using relevant metrics such as Accuracy, AUC, RMSE, Precision/Recall, or F1-score depending on the business objective. I can also include retraining instructions so your internal team can easily reproduce and maintain the workflow. If needed, I can design a clean Power BI dashboard to present predictions, KPIs, and actionable insights in a stakeholder-friendly format. My focus will be on creating models that outperform your current baselines while ensuring the codebase remains organised, scalable, and easy for analysts to understand and extend. I’d be glad to discuss your datasets, current benchmarks, and project timeline in more detail.

@umairinayat1

As an AI/ML engineer, I'm confident that I'm well-equipped to help you achieve your objectives with my broad knowledge and experience in end-to-end ML development. From detecting and tracking objects to OCR and text classification, I've utilized various machine learning algorithms to create models that have consistently outperformed established benchmarks. I specialize in using Python libraries such as pandas, scikit-learn, and XGBoost to manipulate structured data, which I believe aligns perfectly with your project's requirements. I can deliver high-quality Jupyter notebooks/scripts that document the entire modeling pipeline -- from feature engineering to hyperparameter tuning -- enabling reproducibility for your team. Additionally, given my proficiency in backend APIs and Power BI integration, I'll be able to offer a seamless user experience by providing you with serialized model files alongside clear instructions for re-training. My main focus is on delivering code that any intermediate-level analyst would feel comfortable harnessing without extensive assistance. Let's work together to unlock fresh business insights and exceed your current baseline models.

@niveditaj1

You want a reproducible Python predictive modeling pipeline and interactive Power BI dashboard that converts raw sales and database records into clear customer insights. This predictive system extracts clear business opportunities from your transaction logs, enabling you to anticipate market changes and forecast outcomes with high confidence. By visualizing these complex statistical forecasts inside clear Power BI reports, your leadership team gains immediately actionable insights without getting bogged down in complex code. You get complete peace of mind with a fully documented, repeatable workflow that allows your internal team to run updates as new data arrives. We will engineer the machine learning pipeline in Python using pandas for data cleaning, scikit learn for traditional algorithms, and XGBoost to handle non linear patterns. The training code will employ cross validation and GridSearch for hyperparameter tuning, exporting the optimized model files as serialized joblib objects. The visualization layer will connect Power BI directly to the model outputs via local file paths or SQL databases to display key features and active predictions.

@adityabhalsod007

Hi, Your project is a great match for my background in Python, data processing, analytics, and machine learning. I understand that the objective is not simply to build models, but to create a reproducible analytics pipeline that delivers measurable business value and can be maintained by your team long after delivery. My approach would be to develop a complete ML workflow covering data validation, feature engineering, model selection, hyperparameter optimization, evaluation, and deployment-ready model packaging. Depending on the use case, I can compare multiple algorithms including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, Linear Regression, and other suitable models to identify the best-performing solution. Deliverables would include: • Clean, well-documented Python notebooks/scripts • End-to-end modeling pipeline • Performance comparison report with business-focused insights • Serialized models and retraining instructions • Reproducible workflow and documentation • Optional Power BI dashboard with KPIs, trends, predictions, and interactive filtering As someone who works extensively with Python, pandas, scikit-learn, data analysis, and automation, I focus on building solutions that are accurate, explainable, and easy for analysts to extend in the future. I'd be happy to review your dataset structure, target variables, and current baseline performance to propose the most effective modeling strategy. Best regards, Aditya

@sjohn117

Hi, I’m Sean, an AI & Full-Stack Developer with over 10 years of experience, specializing in Python-based analytics and machine learning. I understand you need robust classification and regression models to derive insights from structured data. In similar projects, I developed end-to-end ML pipelines using pandas and scikit-learn, leading to models that significantly enhanced business performance metrics. I can create well-documented Python notebooks for your complete modeling pipeline, including feature engineering, hyper-parameter tuning, and evaluation metrics like accuracy and RMSE. Furthermore, I will provide serialized model files and clear guidelines for re-training, ensuring your team can reproduce results with ease. If required, I can also develop a Power BI dashboard to visualize key findings and predictions. I anticipate delivering the first working milestone within a week to kick off our collaboration. Can you share any specific metrics you currently use to evaluate model performance? Best regards, Sean

@zaina957

Experienced AI/ML Engineer with proven ML projects. I can deliver this accurately and on time. Let’s talk!

@nidhuzalavadiya

Hi there, I have extensive experience building end-to-end Machine Learning pipelines and interactive analytics dashboards, having recently designed a Power Query sales dashboard and smart data-classification logic to process structured financial datasets. For your project, I will deliver clean, production-ready Python notebooks utilizing Pandas, Scikit-Learn, and XGBoost to handle everything from robust feature engineering to automated hyperparameter tuning. I will provide a concise model evaluation report detailing clear performance metrics (AUC, RMSE, and Accuracy) alongside your serialized model files and reproducible training scripts. Additionally, I will build a polished Power BI dashboard to surface key predictive insights and trends for your stakeholders. Let's connect. Best Regards, Nidhi Zalavadiya

@ananya738

Hi, Your project aligns well with my current focus on data analytics, machine learning, and business intelligence workflows. I can help build a clean, reproducible modelling pipeline using Python, pandas, scikit-learn, and XGBoost, covering preprocessing, feature engineering, model training, hyperparameter tuning, and final evaluation. I can deliver well-documented notebooks/scripts, model comparison reports with relevant metrics (AUC, accuracy, RMSE, MAE, etc.), serialized model files, and clear retraining instructions so your team can reproduce results independently. I also have experience creating stakeholder-friendly dashboards and can build a Power BI dashboard to surface predictions, trends, KPIs, and actionable insights. My focus is not just on achieving strong model performance, but also on writing maintainable code and presenting results clearly for business use. Since your datasets are already structured, I can concentrate on optimisation, experimentation, and extracting meaningful insights efficiently. I’d be interested in learning more about the dataset size, target variables, and your current baseline models. Best regards, Ananya

@radhikasinghr2

Hi, I'm a fresher in the data science field, but what I bring is genuine passion, strong fundamentals, and the drive to deliver results that actually matter to your business. Your project excites me because it covers exactly what I've been building my skills around — structured data, Python-based ML pipelines, and meaningful visualisation. Here's what I'll deliver: • Clean, well-commented notebooks using pandas, scikit-learn, and XGBoost covering the full pipeline from feature engineering to evaluation • A clear model comparison report with metrics like AUC, RMSE, or accuracy • Serialized model files + retraining instructions your team can follow easily • Power BI dashboard connecting predictions and insights for stakeholders I may not have years of client experience, but I make up for it with attention to detail, clean code, and a genuine eagerness to go the extra mile to make sure you're satisfied with the output. This would be my first freelance project and I'd give it 100% of my effort to make sure it becomes a success for both of us. Could you share your dataset or describe the target variable? I'd love to get started! Thank you for considering me! Radhika