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I need to turn historical patient-record data into a reliable model that predicts individual outcomes and flags high-risk cases early. The raw material is a sizeable set of anonymised electronic health records: demographics, vitals, lab results, admission notes, discharge summaries—typical structured tables mixed with a handful of free-text fields that have already been de-identified. Here is what I’m after: • A clean, reproducible pipeline in Python and SQL that pulls the records, handles missing values, encodes categorical variables, and outputs a model-ready dataset. • One or more predictive models built in scikit-learn (feel free to benchmark XGBoost or similar) together with well-explained feature importance. AUROC is the key metric; I’d like to see cr...

Desarrollo de IA para Mantenimiento Predictivo y Preventivo de Cintas Transportadoras Logísticas Descripción del Proyecto Busco un desarrollador o equipo especializado en Inteligencia Artificial, Machine Learning e Industria 4.0 para diseñar y desarrollar una solución de mantenimiento predictivo y preventivo aplicada a cintas transportadoras utilizadas en centros logísticos, almacenes automatizados y plantas industriales. El objetivo es crear una plataforma inteligente capaz de monitorizar el estado de las cintas transportadoras en tiempo real, detectar anomalías, predecir averías antes de que ocurran y generar recomendaciones automáticas de mantenimiento. ⸻ Objetivos Principales 1. Monitorización en tiempo real La IA de...

I am conducting an ongoing series of studies that investigate new machine-learning algorithms for computer-science and electronics applications. To keep the research engine running, I need a Python-savvy data analyst who can turn structured datasets into solid predictive models and then translate the results into clear, publication-ready writing. Your typical workflow will involve cleaning and feature-engineering data in pandas, building and evaluating models with scikit-learn (and, when helpful, TensorFlow or PyTorch), and producing insightful visualisations. Once the analytics are sound, I’ll count on you to craft well-structured manuscripts—complete with methodology, metrics, figures, and proper citations—that can be submitted to journals or conferences in the IEEE ec...

The project centres on building an AI-powered customer-relationship-management platform where data analytics integration shapes every decision. Advanced personalisation remains a key theme, yet the first milestone is a robust analytics layer able to ingest multi-channel customer data, surface predictive insights in real time, and feed them back into the CRM for automated, highly targeted experiences. To guarantee a product users love, the plan also calls for continuous A/B testing and structured user-feedback analysis. Results from each experiment will loop straight into the design backlog so that UX refinements happen quickly and are always evidence-based. Key deliverables • Analytics engine connected to the CRM database and external data sources, producing actionable dashboa...

I have a raw set of patient records and want to turn it into a functional medicine-recommendation engine built in Python. The system should read symptom text typed by a user, run it through a pre-trained machine-learning model, and return a ranked list of suitable medications. Scope and key tasks • Data pipeline: remove duplicate records and normalise numeric or categorical values so every observation is model-ready. • Feature selection & training: experiment with at least Random Forest and Logistic Regression; pick the best model using accuracy, precision and recall. • Evaluation report: include confusion matrices and a short note explaining why the chosen model performs best. • Lightweight dashboard: a simple Flask or Streamlit interface with one free-tex...

I want to consistently attract qualified home-buyer leads for my real estate business by harnessing AI. The core of the job is to design and run a data-backed system that blends predictive analytics, natural language processing, and other machine-learning algorithms to pinpoint people who are ready to purchase property, capture their contact information, and deliver those leads to me in an actionable format. Here is what I need from you: • Define or source the data you will feed the models (market listings, consumer-behaviour data, social signals, etc.) and outline how you will keep it refreshed. • Build or fine-tune the models, explain the logic behind your feature selection, and show how the three techniques named above complement one another. • Set up an automated w...

We intends to implement a comprehensive Electronic Document Management System (EDMS) as part of strategic digital transformation roadmap. The project aims to facilitate a phased yet complete transition from a traditional paper-based archive environment to a centralized, modern, and secure electronic document management platform. The key objectives of this initiative are: Improve the efficiency and speed of document retrieval and information access. Enhance protection of corporate records and information assets against loss, damage, or disasters. Strengthen governance, compliance, regulatory adherence, and audit readiness. Reduce operational costs associated with physical document storage and archive management. Support business continuity and operational excellence through secure...

Seeking highly advanced Python/AI pattern-detection specialist with experience in market microstructure, behavioural analytics, anomaly detection, and bookmaker odds movement analysis. Project involves building a rerunnable AI-assisted analytics framework designed to detect and classify pre-match betting market behaviour across global football leagues. Core objective is to distinguish between: • genuine information-driven market movement • liquidity/liability-based bookmaker gearing • deceptive or behavioural drift patterns • ambiguous market behaviour The system must analyse: • Moneyline markets • Over/Under markets • Multi-bookmaker movement concurrence • League-specific behavioural fingerprints • Provider-specific movement bias • Margin ...

I’m kicking off a predictive analytics programme for an IT-focused business unit in Pune and need an experienced AI consultant who can wear the architect’s hat from day one. You’ll guide the full lifecycle: shaping the use-cases with stakeholders, auditing our existing data assets, then designing a scalable machine-learning stack that can move from PoC to production without rewrites. I expect you to be comfortable choosing the right mix of Python, scikit-learn, PyTorch or TensorFlow, setting up automated training pipelines, and wiring everything into a cloud environment (AWS, Azure or GCP—whichever best fits the roadmap we draft together). Strong MLOps habits, version-controlled experiments and CI/CD for models are must-haves because this project must be repeatable...

I need a formally structured expert-opinion letter that speaks with authority on the application of data science in healthcare. The exact sub-topic—whether predictive analytics, patient data management, or AI-driven diagnostics—will be confirmed with you once we connect, so please feel comfortable drawing on whichever of these domains best matches your own credentials. The letter will be shared with senior decision-makers, so it must be: • Written in clear, non-promotional language that balances technical depth with accessibility. • Backed by current literature, relevant guidelines, and any regulatory frameworks that apply (HIPAA, GDPR, FDA, MHRA, etc., as appropriate). • Signed with your professional title, affiliation, and contact details, plus a brief bi...

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