Need Ml engineer for one task

@amgadmourad18

Hello , I'm a Python/data science developer with strong experience in numerical data processing and machine learning using Pandas, Scikit-learn, XGBoost and PyTorch. I can take the dataset through the full pipeline: data cleaning and EDA, feature engineering, train/test strategy, XGBoost training and hyperparameter tuning with k-fold cross-validation. I’ll also pay attention to class imbalance and threshold tuning so the final F1 score is optimized rather than focusing only on accuracy. The deliverables will include a clean and reproducible Python notebook/script, README, trained model file, and full evaluation including accuracy, precision, recall, F1 and confusion matrix. I’ll also make sure there is no data leakage between preprocessing and validation. I have worked on ML systems involving structured data, analytics and model-based recommendations, and i can provide relevant examples from my previous Python/ML work. I’m ready to review the dataset first and establish a realistic target F1 before training the final model. Looking forward to work with you, thanks.

@billybryan98

Hello there. I hope you are donig well. I have successfully developed and deployed binary classification models using XGBoost in previous projects. My experience with numerical datasets allows me to effectively clean, explore, and engineer features, ensuring that the model performs optimally. I can provide examples showcasing my ability to enhance model accuracy through feature engineering and tuning. I understand that you need a production-ready binary classifier built around XGBoost. I will clean and preprocess the numerical dataset, engineer relevant features, and train the model using k-fold cross-validation to ensure robust performance. My approach will focus on achieving the agreed-upon F1 score for your project. I will deliver well-commented Python code in a single notebook along with a brief README for reproducibility. You will receive performance metrics including accuracy, precision, recall, and F1 score, along with a confusion matrix. Additionally, I will package the trained model for easy deployment into your pipeline, ensuring high-quality results. Please feel free to reach out to me. I look forward to working with you. Best regards, Billy Bryan

@codeandcommand

Production-ready is the key word here, not just a working model. I'd start with proper train/test splits, handle class imbalance if it exists, then wrap the classifier behind a clean API so it's actually deployable, not just a notebook. I can start today and have a first version running within 3 days. The budget and timeline reflect the post as written. We'll firm both up once we go through the dataset together. Want to jump on a quick call?

@robertb297

I can help turn your numerical dataset into a production-ready XGBoost binary classifier. I have experience with Python, feature engineering, model selection, cross-validation, and evaluation using metrics such as precision, recall, F1, and confusion matrices. I’ll clean and explore the data, engineer relevant features, tune XGBoost with k-fold cross-validation, and provide reproducible, well-commented code. I’ll also package the final trained model using Joblib/Pickle and include a concise README with the workflow and results. I can start by inspecting the dataset and establishing a reliable baseline before optimizing toward the agreed F1 target.

@ayoubt14

I’ll first inspect the dataset and establish a baseline so we can agree on a realistic F1 acceptance target based on the data’s characteristics. From there, I’ll focus the tuning effort on achieving the best generalization rather than simply maximizing cross-validation performance. The final deliverable will be a model and codebase that you can actually plug into your pipeline, not just a notebook showing an experimental result. I’m ready to get started as soon as you provide the dataset and target-column details.

@adarsht201

Hello, I can build the requested production-ready binary classification solution using Python and XGBoost. My approach will include: - Cleaning and exploring the numerical dataset - Feature engineering and preprocessing - XGBoost model training and hyperparameter tuning - K-fold cross-validation - Evaluation using accuracy, precision, recall, and F1-score - Confusion matrix and performance analysis - Well-commented Python code in a notebook or script - Reproducible README with setup and usage instructions - Saving the trained model using Joblib or Pickle for deployment I have experience working with Python, Pandas, NumPy, Scikit-learn, and machine learning workflows including data preprocessing, model training, and evaluation. I can first inspect the dataset and establish a baseline F1-score, then optimize the model toward the agreed target while avoiding data leakage and overfitting. I’m ready to start and can provide a clean, reproducible implementation.

@SAJJADHAIDER3

Hello, I can help build a production-ready XGBoost binary classification model from your numerical dataset and provide a complete, reproducible solution. My workflow includes: • Data cleaning, preprocessing, and exploratory analysis • Feature engineering and feature selection for improved performance • XGBoost training with hyperparameter tuning • K-fold cross-validation to ensure robust generalization • Performance evaluation using Accuracy, Precision, Recall, F1 Score, and Confusion Matrix • Delivery of clean, well-commented Python code (Jupyter Notebook or script) • Trained model export (Joblib, Pickle, or ONNX) ready for deployment • Brief README with setup and reproduction instructions I have experience working with Python, Machine Learning, Data Analysis, and model optimization, and I focus on building models that perform well not only on training data but also on unseen data. After reviewing the dataset, I can provide an initial assessment and discuss a realistic target F1 score before finalizing the model. Tools & Libraries:Python, XGBoost, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn I can start immediately and deliver a reliable, well-documented solution within the agreed timeline. Best regards, Sajjad Haider

@akankshat18

Hi, I can build the requested XGBoost binary classification solution in Python, including data cleaning, exploratory analysis, feature engineering, model training and hyperparameter tuning. I will use k-fold cross-validation and provide accuracy, precision, recall, F1-score, and a confusion matrix to properly evaluate the model. I’ll also provide clean, well-commented Python code in a notebook/script, a short README for reproducibility, and package the final trained model using joblib or pickle. I have experience working with Python, Pandas, scikit-learn and machine learning workflows, and can structure the solution so it is easy to integrate into your existing pipeline. I’m happy to first inspect the dataset and discuss a realistic F1-score target before finalizing the model. Regards, Akanksha

@AizmaTech

Hi, I can build your complete XGBoost binary classifier, including data cleaning, EDA, feature engineering, hyperparameter tuning, Stratified K-Fold CV, and evaluation with accuracy, precision, recall, F1, and confusion matrix. I’ve worked on numerical ML projects involving feature extraction/selection, SMOTE, StratifiedKFold, and classification pipelines, including Human Activity Recognition with time-domain and FFT features. I also have experience with YOLOv8, PyTorch, OpenCV, and deep-learning projects. You’ll receive clean, commented code, a reproducible README, and the trained model in joblib/pickle/ONNX format. Payment will only be requested once the agreed F1 acceptance criterion has been met. I can start by reviewing the dataset and establishing a realistic F1 target.

@MohammadkarimKz

Hello, I have more than 4 years extensive experience in AI developing by Python and website development by Javascript. I have many expert projects in Machine Learning, Deep Learning and Image-Processing. I have spirit of team-working, collaborative, cooperative and easy-learner. I am always ready to get involved in any projects with any scales and problems and always keen in problem solving.