Despliegue de Modelos Jobs

Project Brief I want to build Version 2 of my existing mobile Web3 wallet app. V2 should include a UI revamp, additional features, and end-to-end deployment support. Current status: Existing app is live and codebase is available. Backend services/APIs are already deployed. Goal is to create a cleaner, more modern V2 without disrupting current production users. Tech stack: React Native with Expo. Expo Router. NativeWind/Tailwind. TanStack Query + Context. Existing wallet/transaction integrations. What I want in Version 2: Full UI redesign across key flows (modern, consistent, polished). New features I will provide in a detailed list. Better UX for onboarding, wallet actions, and transaction flows. Refactor frontend structure for maintainability and scalability. Keep backend compatibilit...

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...